diff --git a/NEWS.md b/NEWS.md
index d5428ba1..3cf899fa 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -17,6 +17,10 @@
   * `heatmap_fill_colour_rev`: a logical value that specifies if the colour gradient should be reversed.
   * `plot_cutoff`: is now more flexible. You can provide any number with the "top" cutoff. E.g. "top10", "top5".
 * Added `mako_colours` to the package that contain 256 colours of the "mako" colour gradient.
+* `fit_drc_4p()` and `parallel_fit_drc_4p()` have been reworked and you will now likely have a different output than in previous versions of *protti*. We added a new argument (`anova_cutoff`) that lets you define the ANOVA adjusted p-value cutoff (default 0.05). In addition, curves that were previously annotated in the `dose_MNAR` column are now part of the hits. To get back to the old output you can just exclude them again from the ranked results. The major change is that now all provided features (e.g. peptides) are also part of the output no matter if a curve was fit or not. To get back to the original output you can remove all features without a fit, but please note that statistics such as the ANOVA p-value adjustment were computed on the complete dataset and might need to be readjusted by running the p-value adjustment again. Another major change to the function was the way the `filter` argument works. This argument controls if significance statistics should be annotated in the data. 
+  * `"pre"`: This prevously filtered curves by the completeness as well as the ANOVA adjusted p-value prior to fitting curves. Now it only filters by completeness. This also allows it to be an option for the `parallel_fit_drc_4p()` function. 
+  * `"post"`: Is still the default value and still just annotates the data without any filtering.
+  * In general we would now recommend using `"pre"` to remove usually not trustworthy features with too few complete concentrations from the data before p-value adjustment and curve fittings. This will solidify your confidence that features without a dose-response behavior are true negative. The point is that it is better to not include any features with too few values because they are potentially false negative.
 
 ## Bug fixes
 
diff --git a/R/fit_drc_4p.R b/R/fit_drc_4p.R
index 88796a60..5f6e7fef 100644
--- a/R/fit_drc_4p.R
+++ b/R/fit_drc_4p.R
@@ -1,41 +1,49 @@
 #' Fitting four-parameter dose response curves
 #'
 #' Function for fitting four-parameter dose response curves for each group (precursor, peptide or
-#' protein). In addition it can filter data based on completeness, the completeness distribution
-#' and statistical testing using ANOVA.
+#' protein). In addition it can annotate data based on completeness, the completeness distribution
+#' and statistical testing using ANOVA. Filtering by the function is only performed based on completeness
+#' if selected.
 #'
-#' @details If data filtering options are selected, data is filtered based on multiple criteria.
-#' In general, curves are only fitted if there are at least 5 conditions with data points present
-#' to ensure that there is potential for a good curve fit. Therefore, this is also the case if no
-#' filtering option is selected. Furthermore, a completeness cutoff is defined for filtering. By
-#' default each entity (e.g. precursor) is filtered to contain at least 70% of total replicates
-#' (adjusted downward) for at least 50% of all conditions (adjusted downward). This can be adjusted
-#' with the according arguments. In addition to the completeness cutoff, also a significance cutoff
-#' is applied. ANOVA is used to compute the statistical significance of the change for each entity.
-#' The resulting p-value is adjusted using the Benjamini-Hochberg method and a cutoff of q <= 0.05
-#' is applied. Curve fits that have a minimal value that is higher than the maximal value are
-#' excluded as they were likely wrongly fitted. Curves with a correlation below 0.8 are not passing
-#' the filtering. If a fit does not fulfill the significance or completeness cutoff, it has a chance
-#' to still be considered if half of its values (+/-1 value) pass the replicate completeness
-#' criteria and half do not pass it. In order to fall into this category, the values that fulfill t
-#' he completeness cutoff and the ones that do not fulfill it need to be consecutive, meaning
-#' located next to each other based on their concentration values. Furthermore, the values that
-#' do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
+#' @details If data filtering options are selected, data is annotated based on multiple criteria.
+#' If `"post"` is selected the data is annotated based on completeness, the completeness distribution, the
+#' adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on
+#' the `replicate_completeness` and `condition_completeness` arguments. The completeness distribution determines
+#' if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a
+#' features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into
+#' this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it
+#' need to be consecutive, meaning located next to each other based on their concentration values. Furthermore,
+#' the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
 #' between the two groups is tested for statistical significance using a Welch's t-test and a
-#' cutoff of p <= 0.1 (we want to mainly discard curves that falsly fit the other criteria but that
+#' cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that
 #' have clearly non-significant differences in mean). This allows curves to be considered that have
 #' missing values in half of their observations due to a decrease in intensity. It can be thought
 #' of as conditions that are missing not at random (MNAR). It is often the case that those entities
 #' do not have a significant p-value since half of their conditions are not considered due to data
-#' missingness.
+#' missingness. The ANOVA test is performed on the features by concentration. If it is significant it is
+#' likely that there is some response. However, this test would also be significant even if there is one
+#' outlier concentration so it should only be used only in combination with other cutoffs to determine
+#' if a feature is significant. The `passed_filter` column is `TRUE` for all the
+#' features that pass the above mentioned criteria and that have a correlation greater than the cutoff
+#' (default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).
 #'
-#' The final filtered list is ranked based on a score calculated on entities that pass the filter.
+#' The final list is ranked based on a score calculated on entities that pass the filter.
 #' The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and the
 #' correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an
 #' entity can have is 1 with both the highest correlation and adjusted p-value. The rank is
 #' corresponding to this score. Please note, that entities with MNAR conditions might have a
-#' lower score due to the missing or non-significant ANOVA p-value. You should have a look at
-#' curves that are TRUE for \code{dose_MNAR} in more detail.
+#' lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated
+#' the usual way these cases receive a score of 0. You should have a look at curves that are TRUE
+#' for \code{dose_MNAR} in more detail.
+#'
+#' If the `"pre"` option is selected for the `filter` argument then the data is filtered for completeness
+#' prior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.
+#' We recommend the `"pre"` option because it leaves you with not only the likely hits of your treatment, but
+#' also with rather high confidence true negative results. This is because the filtered data has a high
+#' degree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.
+#'
+#' Please note that in general, curves are only fitted if there are at least 5 conditions with data points present
+#' to ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.
 #'
 #' @param data a data frame that contains at least the input variables.
 #' @param sample  a character column in the \code{data} data frame that contains the sample names.
@@ -45,17 +53,15 @@
 #' values, e.g. log2 transformed intensities.
 #' @param dose  a numeric column in the \code{data} data frame that contains the dose values, e.g.
 #' the treatment concentrations.
-#' @param filter a character value that determines if models should be filtered and if they should
-#' be filtered before or after the curve fits. Filtering of models can be skipped with
-#' \code{filter = "none"}. Data can be filtered prior to model fitting with \code{filter = "pre"}.
-#' In that case models will only be fitted for data that passed the filtering step. This will
-#' allow for faster model fitting since only fewer models will be fit. If you plan on performing
-#' an enrichment analysis you have to choose \code{filter = "post"}. All models will be fit (even
-#' the ones that do not pass the filtering criteria). For enrichment analysis you should use both
-#' good (i.e. models that pass the filtering) and bad (i.e. models that do not pass the filtering)
-#' models. Therefore, for post-filtering the full list is returned and it will only contain
-#' annotations that indicate (\code{passed_filter}) if the filtering was passed or not. Default is
-#' "post". For ANOVA an adjusted p-value of 0.05 is used as a cutoff.
+#' @param filter a character value that can either be `"pre"`, `"post"` or `"none"`. The data is
+#' annotated for completeness, ANOVA significance and the completeness distribution along
+#' the doses (`"pre"` and `"post"`). The combined output of this filtering step can be found in
+#' the `passed_filter` column and depends on the cutoffs provided to the function. Note that this
+#' is only an annotation and nothing is removed from the output. If `"pre"` is selected then, in
+#' addition to the annotation, the data is filtered for completeness based on the condition completeness
+#' prior to the curve fitting and ANOVA calculation and p-value adjustment. This has the benefit that less
+#' curves need to be fitted and that the ANOVA p-value adjustment is done only on the relevant set of tests.
+#' If `"none"` is selected the data will be neither annotated nor filtered.
 #' @param replicate_completeness a numeric value which similar to \code{completenss_MAR} of the
 #' \code{assign_missingness} function sets a threshold for the completeness of data. In contrast
 #' to \code{assign_missingness} it only determines the completeness for one condition and not the
@@ -70,8 +76,11 @@
 #' the number of conditions and then adjusted downward. The resulting number is the minimal number
 #' of conditions that need to fulfill the \code{replicate_completeness} argument for a peptide to
 #' pass the filtering.
-#' @param correlation_cutoff a numeric vector that specifies the correlation cutoff used for data
-#' filtering.
+#' @param anova_cutoff a numeric value that specifies the ANOVA adjusted p-value cutoff used for
+#' data filtering. Any fits with an adjusted ANOVA p-value bellow the cutoff will be considered
+#' for scoring.
+#' @param correlation_cutoff a numeric value that specifies the correlation cutoff used for data
+#' filtering. Any fits with a correlation above the cutoff will be considered for scoring.
 #' @param log_logarithmic a logical value that indicates if a logarithmic or log-logarithmic model
 #' is fitted. If response values form a symmetric curve for non-log transformed dose values, a
 #' logarithmic model instead of a log-logarithmic model should be used. Usually biological dose
@@ -143,6 +152,7 @@ fit_drc_4p <- function(data,
                        filter = "post",
                        replicate_completeness = 0.7,
                        condition_completeness = 0.5,
+                       anova_cutoff = 0.05,
                        correlation_cutoff = 0.8,
                        log_logarithmic = TRUE,
                        include_models = FALSE,
@@ -173,27 +183,6 @@ fit_drc_4p <- function(data,
     n_replicates_completeness <- floor(replicate_completeness * n_replicates)
     n_conditions_completeness <- floor(condition_completeness * n_conditions)
 
-    # perform anova on groups
-    anova <- data_prep %>%
-      dplyr::group_by({{ grouping }}, {{ dose }}) %>%
-      dplyr::mutate(
-        n = n_replicates,
-        mean_ratio = mean({{ response }}, na.rm = TRUE),
-        sd = sd({{ response }}, na.rm = TRUE)
-      ) %>%
-      dplyr::distinct({{ grouping }}, {{ dose }}, .data$mean_ratio, .data$sd, .data$n) %>%
-      tidyr::drop_na("mean_ratio", "sd") %>%
-      anova_protti({{ grouping }}, {{ dose }}, .data$mean_ratio, .data$sd, .data$n) %>%
-      dplyr::distinct({{ grouping }}, .data$pval) %>%
-      tidyr::drop_na("pval") %>% # remove NA pvalues before adjustment!
-      dplyr::mutate(anova_adj_pval = stats::p.adjust(.data$pval, method = "BH")) %>%
-      dplyr::rename(anova_pval = "pval")
-
-    # extract elements that pass anova significant threshold
-    anova_filtered <- anova %>%
-      dplyr::filter(.data$anova_adj_pval <= 0.05) %>%
-      dplyr::pull({{ grouping }})
-
     # filter for data completeness based on replicates and conditions
     # and based on the distribution of condition missingness
     up <- ceiling(n_conditions * 0.5)
@@ -211,12 +200,11 @@ fit_drc_4p <- function(data,
     filter_completeness <- data_prep %>%
       dplyr::group_by({{ grouping }}, {{ dose }}) %>%
       dplyr::mutate(enough_replicates = sum(!is.na({{ response }}))
-      >= n_replicates_completeness) %>%
+                    >= n_replicates_completeness) %>%
       dplyr::group_by({{ grouping }}) %>%
       dplyr::mutate(enough_conditions = sum(.data$enough_replicates) /
-        n_replicates
-      >= n_conditions_completeness) %>%
-      dplyr::mutate(anova_significant = {{ grouping }} %in% anova_filtered) %>%
+                      n_replicates
+                    >= n_conditions_completeness) %>%
       dplyr::mutate(
         lower_vector = {{ dose }} %in% concentrations[1:vector[1]],
         lower_vector_rev = {{ dose }} %in% concentrations[1:vector_rev[1]],
@@ -283,37 +271,37 @@ fit_drc_4p <- function(data,
       dplyr::ungroup() %>%
       dplyr::mutate(
         pval_vector = ifelse(is.na(.data$pval_vector),
-          TRUE,
-          .data$pval_vector
+                             TRUE,
+                             .data$pval_vector
         ),
         pval_vector_rev = ifelse(is.na(.data$pval_vector_rev),
-          TRUE,
-          .data$pval_vector_rev
+                                 TRUE,
+                                 .data$pval_vector_rev
         ),
         pval_vector_add = ifelse(is.na(.data$pval_vector_add),
-          TRUE,
-          .data$pval_vector_add
+                                 TRUE,
+                                 .data$pval_vector_add
         ),
         pval_vector_add_rev = ifelse(is.na(.data$pval_vector_add_rev),
-          TRUE,
-          .data$pval_vector_add_rev
+                                     TRUE,
+                                     .data$pval_vector_add_rev
         )
       ) %>%
       dplyr::mutate(mean_vector = ifelse(is.na(.data$mean_vector),
-        0,
-        .data$mean_vector
+                                         0,
+                                         .data$mean_vector
       )) %>%
       dplyr::mutate(mean_vector_rev = ifelse(is.na(.data$mean_vector_rev),
-        0,
-        .data$mean_vector_rev
+                                             0,
+                                             .data$mean_vector_rev
       )) %>%
       dplyr::mutate(mean_vector_add = ifelse(is.na(.data$mean_vector_add),
-        0,
-        .data$mean_vector_add
+                                             0,
+                                             .data$mean_vector_add
       )) %>%
       dplyr::mutate(mean_vector_add_rev = ifelse(is.na(.data$mean_vector_add_rev),
-        0,
-        .data$mean_vector_add_rev
+                                                 0,
+                                                 .data$mean_vector_add_rev
       )) %>%
       dplyr::group_by({{ grouping }}) %>%
       dplyr::mutate(mean_vector = min(.data$mean_vector) == .data$mean_vector) %>%
@@ -321,53 +309,79 @@ fit_drc_4p <- function(data,
       dplyr::mutate(mean_vector_add = min(.data$mean_vector_add) == .data$mean_vector_add) %>%
       dplyr::mutate(mean_vector_add_rev = min(.data$mean_vector_add_rev) == .data$mean_vector_add_rev) %>%
       dplyr::mutate(dose_MNAR = ifelse((all((.data$lower_vector & .data$enough_replicates == FALSE & .data$mean_vector) |
-        (!.data$lower_vector & .data$enough_replicates == TRUE & !.data$mean_vector)) &
-        .data$pval_vector) |
-        (all((.data$lower_vector & .data$enough_replicates == TRUE & !.data$mean_vector) |
-          (!.data$lower_vector & .data$enough_replicates == FALSE & .data$mean_vector)) &
-          .data$pval_vector) |
-        (all((.data$lower_vector_rev & .data$enough_replicates == FALSE & .data$mean_vector_rev) |
-          (!.data$lower_vector_rev & .data$enough_replicates == TRUE & !.data$mean_vector_rev)) &
-          .data$pval_vector_rev) |
-        (all((.data$lower_vector_rev & .data$enough_replicates == TRUE & !.data$mean_vector_rev) |
-          (!.data$lower_vector_rev & .data$enough_replicates == FALSE & .data$mean_vector_rev)) &
-          .data$pval_vector_rev) |
-        (all((.data$lower_vector_add & .data$enough_replicates == FALSE & .data$mean_vector_add) |
-          (!.data$lower_vector_add & .data$enough_replicates == TRUE & !.data$mean_vector_add)) &
-          .data$pval_vector_add) |
-        (all((.data$lower_vector_add & .data$enough_replicates == TRUE & !.data$mean_vector_add) |
-          (!.data$lower_vector_add & .data$enough_replicates == FALSE & .data$mean_vector_add)) &
-          .data$pval_vector_add) |
-        (all((.data$lower_vector_add_rev & .data$enough_replicates == FALSE & .data$mean_vector_add_rev) |
-          (!.data$lower_vector_add_rev & .data$enough_replicates == TRUE & !.data$mean_vector_add_rev)) &
-          .data$pval_vector_add_rev) |
-        (all((.data$lower_vector_add_rev & .data$enough_replicates == TRUE & !.data$mean_vector_add_rev) |
-          (!.data$lower_vector_add_rev & .data$enough_replicates == FALSE & .data$mean_vector_add_rev)) &
-          .data$pval_vector_add_rev),
-      TRUE,
-      FALSE
+                                              (!.data$lower_vector & .data$enough_replicates == TRUE & !.data$mean_vector)) &
+                                          .data$pval_vector) |
+                                         (all((.data$lower_vector & .data$enough_replicates == TRUE & !.data$mean_vector) |
+                                                (!.data$lower_vector & .data$enough_replicates == FALSE & .data$mean_vector)) &
+                                            .data$pval_vector) |
+                                         (all((.data$lower_vector_rev & .data$enough_replicates == FALSE & .data$mean_vector_rev) |
+                                                (!.data$lower_vector_rev & .data$enough_replicates == TRUE & !.data$mean_vector_rev)) &
+                                            .data$pval_vector_rev) |
+                                         (all((.data$lower_vector_rev & .data$enough_replicates == TRUE & !.data$mean_vector_rev) |
+                                                (!.data$lower_vector_rev & .data$enough_replicates == FALSE & .data$mean_vector_rev)) &
+                                            .data$pval_vector_rev) |
+                                         (all((.data$lower_vector_add & .data$enough_replicates == FALSE & .data$mean_vector_add) |
+                                                (!.data$lower_vector_add & .data$enough_replicates == TRUE & !.data$mean_vector_add)) &
+                                            .data$pval_vector_add) |
+                                         (all((.data$lower_vector_add & .data$enough_replicates == TRUE & !.data$mean_vector_add) |
+                                                (!.data$lower_vector_add & .data$enough_replicates == FALSE & .data$mean_vector_add)) &
+                                            .data$pval_vector_add) |
+                                         (all((.data$lower_vector_add_rev & .data$enough_replicates == FALSE & .data$mean_vector_add_rev) |
+                                                (!.data$lower_vector_add_rev & .data$enough_replicates == TRUE & !.data$mean_vector_add_rev)) &
+                                            .data$pval_vector_add_rev) |
+                                         (all((.data$lower_vector_add_rev & .data$enough_replicates == TRUE & !.data$mean_vector_add_rev) |
+                                                (!.data$lower_vector_add_rev & .data$enough_replicates == FALSE & .data$mean_vector_add_rev)) &
+                                            .data$pval_vector_add_rev),
+                                       TRUE,
+                                       FALSE
       )) %>%
-      dplyr::distinct({{ grouping }}, .data$enough_conditions, .data$anova_significant, .data$dose_MNAR) %>%
-      dplyr::mutate(passed_filter = (.data$enough_conditions == TRUE & .data$anova_significant == TRUE) |
-        .data$dose_MNAR == TRUE)
+      dplyr::distinct({{ grouping }}, .data$enough_conditions, .data$dose_MNAR)
 
     if (filter == "pre") {
       filtered_vector <- filter_completeness %>%
-        dplyr::filter(.data$passed_filter == TRUE) %>%
+        dplyr::filter(.data$enough_conditions | .data$dose_MNAR) %>%
         dplyr::pull({{ grouping }})
 
       data_prep <- data_prep %>%
         filter({{ grouping }} %in% filtered_vector)
     }
+
+    # perform anova on groups
+    anova <- data_prep %>%
+      dplyr::group_by({{ grouping }}, {{ dose }}) %>%
+      dplyr::mutate(
+        n = n_replicates,
+        mean_ratio = mean({{ response }}, na.rm = TRUE),
+        sd = sd({{ response }}, na.rm = TRUE)
+      ) %>%
+      dplyr::distinct({{ grouping }}, {{ dose }}, .data$mean_ratio, .data$sd, .data$n) %>%
+      tidyr::drop_na("mean_ratio", "sd") %>%
+      anova_protti({{ grouping }}, {{ dose }}, .data$mean_ratio, .data$sd, .data$n) %>%
+      dplyr::distinct({{ grouping }}, .data$pval) %>%
+      tidyr::drop_na("pval") %>% # remove NA pvalues before adjustment!
+      dplyr::mutate(anova_adj_pval = stats::p.adjust(.data$pval, method = "BH")) %>%
+      dplyr::rename(anova_pval = "pval")
+
+    # extract elements that pass anova significant threshold
+    anova_filtered <- anova %>%
+      dplyr::filter(.data$anova_adj_pval <= anova_cutoff) %>%
+      dplyr::pull({{ grouping }})
+
+    filter_completeness <- filter_completeness %>%
+      dplyr::mutate(anova_significant = {{ grouping }} %in% anova_filtered) %>%
+      dplyr::mutate(passed_filter = (.data$enough_conditions == TRUE & .data$anova_significant == TRUE) |
+                      .data$dose_MNAR == TRUE)
   }
   # prepare data
 
-  input <- data_prep %>%
+  input_prep <- data_prep %>%
     tidyr::drop_na({{ response }}) %>%
     dplyr::group_by({{ grouping }}) %>%
     dplyr::mutate(n_concentrations = dplyr::n_distinct(!!ensym(dose))) %>%
     dplyr::ungroup() %>%
-    split(dplyr::pull(., !!ensym(grouping))) %>%
+    split(dplyr::pull(., !!ensym(grouping)))
+
+  input <- input_prep %>%
     # make sure that there are enough data points to even fit a curve. This is not really filtering.
     purrr::keep(.p = ~ unique(.x$n_concentrations) > 4)
 
@@ -512,7 +526,7 @@ fit_drc_4p <- function(data,
       .f = ~ dplyr::mutate(.x, {{ grouping }} := .y)
     )
 
-  plot_points <- input[unique(dplyr::pull(line_fit, {{ grouping }}))] %>%
+  plot_points <- input_prep %>%
     purrr::map2_df(
       .y = names(.),
       .f = ~ dplyr::mutate(.x, {{ grouping }} := .y) %>%
@@ -521,7 +535,11 @@ fit_drc_4p <- function(data,
 
   # combining correlations with information for plot
 
+  no_fits <- as.data.frame(names(input_prep)[!names(input_prep) %in% pull(correlation_output, {{ grouping }})])
+  colnames(no_fits) <- rlang::as_name(rlang::enquo(grouping))
+
   output <- correlation_output %>%
+    bind_rows(no_fits) %>%
     dplyr::left_join(line_fit, by = rlang::as_name(rlang::enquo(grouping))) %>%
     dplyr::group_by({{ grouping }}) %>%
     tidyr::nest(plot_curve = c("dose", "Prediction", "Lower", "Upper")) %>%
@@ -541,22 +559,18 @@ fit_drc_4p <- function(data,
     output <- output %>%
       dplyr::left_join(filter_completeness, by = rlang::as_name(rlang::enquo(grouping))) %>%
       dplyr::left_join(anova, by = rlang::as_name(rlang::enquo(grouping))) %>%
-      dplyr::mutate(passed_filter = .data$passed_filter &
+      dplyr::mutate(passed_filter = (.data$passed_filter &
         .data$correlation >= correlation_cutoff &
-        .data$min_model < .data$max_model) %>%
+        .data$min_model < .data$max_model) |
+          .data$dose_MNAR) %>%
       dplyr::group_by(.data$passed_filter) %>%
-      dplyr::mutate(score = ifelse(.data$passed_filter,
+      dplyr::mutate(score = ifelse(.data$passed_filter & !.data$dose_MNAR,
         (scale_protti(-log10(.data$anova_pval), method = "01") + scale_protti(.data$correlation, method = "01")) / 2,
         NA
       )) %>%
-      dplyr::ungroup()
-  }
-
-  if (filter == "pre") {
-    output <- output %>%
-      filter(.data$passed_filter)
+      dplyr::ungroup() %>%
+      dplyr::mutate(score = ifelse(is.na(.data$score) & .data$passed_filter == TRUE, 0, .data$score))
   }
-  # return result
 
   if (!missing(retain_columns)) {
     output <- data %>%
diff --git a/R/parallel_fit_drc_4p.R b/R/parallel_fit_drc_4p.R
index be4c3365..59eef742 100644
--- a/R/parallel_fit_drc_4p.R
+++ b/R/parallel_fit_drc_4p.R
@@ -9,38 +9,45 @@
 #' individual fit objects when using this function as compared to the non parallel function as
 #' they are too large for efficient export from the workers.
 #'
-#' @details If data filtering options are selected, data is filtered based on multiple criteria.
-#' In general, curves are only fitted if there are at least 5 conditions with data points present
-#' to ensure that there is potential for a good curve fit. Therefore, this is also the case if no
-#' filtering option is selected. Furthermore, a completeness cutoff is defined for filtering. By
-#' default each entity (e.g. precursor) is filtered to contain at least 70% of total replicates
-#' (adjusted downward) for at least 50% of all conditions (adjusted downward). This can be adjusted
-#' with the according arguments. In addition to the completeness cutoff, also a significance cutoff
-#' is applied. ANOVA is used to compute the statistical significance of the change for each entity.
-#' The resulting p-value is adjusted using the Benjamini-Hochberg method and a cutoff of q <= 0.05
-#' is applied. Curve fits that have a minimal value that is higher than the maximal value are
-#' excluded as they were likely wrongly fitted. Curves with a correlation below 0.8 are not passing
-#' the filtering. If a fit does not fulfill the significance or completeness cutoff, it has a
-#' chance to still be considered if half of its values (+/-1 value) pass the replicate completeness
-#' criteria and half do not pass it. In order to fall into this category, the values that fulfill
-#' the completeness cutoff and the ones that do not fulfill it need to be consecutive, meaning
-#' located next to each other based on their concentration values. Furthermore, the values that
-#' do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
+#' @details If data filtering options are selected, data is annotated based on multiple criteria.
+#' If `"post"` is selected the data is annotated based on completeness, the completeness distribution, the
+#' adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on
+#' the `replicate_completeness` and `condition_completeness` arguments. The completeness distribution determines
+#' if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a
+#' features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into
+#' this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it
+#' need to be consecutive, meaning located next to each other based on their concentration values. Furthermore,
+#' the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
 #' between the two groups is tested for statistical significance using a Welch's t-test and a
-#' cutoff of p <= 0.1 (we want to mainly discard curves that falsly fit the other criteria
-#' but that have clearly non-significant differences in mean). This allows curves to be considered
-#' that have missing values in half of their observations due to a decrease in intensity. It can
-#' be thought of as conditions that are missing not at random (MNAR). It is often the case that
-#' those entities do not have a significant p-value since half of their conditions are not
-#' considered due to data missingness.
+#' cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that
+#' have clearly non-significant differences in mean). This allows curves to be considered that have
+#' missing values in half of their observations due to a decrease in intensity. It can be thought
+#' of as conditions that are missing not at random (MNAR). It is often the case that those entities
+#' do not have a significant p-value since half of their conditions are not considered due to data
+#' missingness. The ANOVA test is performed on the features by concentration. If it is significant it is
+#' likely that there is some response. However, this test would also be significant even if there is one
+#' outlier concentration so it should only be used only in combination with other cutoffs to determine
+#' if a feature is significant. The `passed_filter` column is `TRUE` for all the
+#' features that pass the above mentioned criteria and that have a correlation greater than the cutoff
+#' (default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).
 #'
-#' The final filtered list is ranked based on a score calculated on entities that pass the filter.
+#' The final list is ranked based on a score calculated on entities that pass the filter.
 #' The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and the
 #' correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an
 #' entity can have is 1 with both the highest correlation and adjusted p-value. The rank is
 #' corresponding to this score. Please note, that entities with MNAR conditions might have a
-#' lower score due to the missing or non-significant ANOVA p-value. You should have a look at
-#' curves that are TRUE for \code{dose_MNAR} in more detail.
+#' lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated
+#' the usual way these cases receive a score of 0. You should have a look at curves that are TRUE
+#' for \code{dose_MNAR} in more detail.
+#'
+#' If the `"pre"` option is selected for the `filter` argument then the data is filtered for completeness
+#' prior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.
+#' We recommend the `"pre"` option because it leaves you with not only the likely hits of your treatment, but
+#' also with rather high confidence true negative results. This is because the filtered data has a high
+#' degree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.
+#'
+#' Please note that in general, curves are only fitted if there are at least 5 conditions with data points present
+#' to ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.
 #'
 #' @param data a data frame that contains at least the input variables.
 #' @param sample  a character column in the \code{data} data frame that contains the sample names.
@@ -50,12 +57,15 @@
 #' values, e.g. log2 transformed intensities.
 #' @param dose  a numeric column in the \code{data} data frame that contains the dose values, e.g.
 #' the treatment concentrations.
-#' @param filter a character value that determines if models should be filtered and if they should
-#' be filtered. The option \code{"pre"} is not available for parallel fitting of models. This is
-#' because ANOVA adjusted p-values would be wrongly calculated because the dataset is split onto
-#' multiple cores. Default is "post" and we recommend always using "post" because compared to
-#' "none" only some additional columns are added that contain the filter information. For ANOVA
-#' an adjusted p-value of 0.05 is used as a cutoff.
+#' @param filter a character value that can either be `"pre"`, `"post"` or `"none"`. The data is
+#' annotated for completeness, ANOVA significance and the completeness distribution along
+#' the doses (`"pre"` and `"post"`). The combined output of this filtering step can be found in
+#' the `passed_filter` column and depends on the cutoffs provided to the function. Note that this
+#' is only an annotation and nothing is removed from the output. If `"pre"` is selected then, in
+#' addition to the annotation, the data is filtered for completeness based on the condition completeness
+#' prior to the curve fitting and ANOVA calculation and p-value adjustment. This has the benefit that less
+#' curves need to be fitted and that the ANOVA p-value adjustment is done only on the relevant set of tests.
+#' If `"none"` is selected the data will be neither annotated nor filtered.
 #' @param replicate_completeness a numeric value which similar to \code{completenss_MAR} of the
 #' \code{assign_missingness} function sets a threshold for the completeness of data. In contrast
 #' to \code{assign_missingness} it only determines the completeness for one condition and not the
@@ -70,8 +80,11 @@
 #' the number of conditions and then adjusted downward. The resulting number is the minimal number
 #' of conditions that need to fulfill the \code{replicate_completeness} argument for a peptide to
 #' pass the filtering.
-#' @param correlation_cutoff a numeric vector that specifies the correlation cutoff used for data
-#' filtering.
+#' @param anova_cutoff a numeric value that specifies the ANOVA adjusted p-value cutoff used for
+#' data filtering. Any fits with an adjusted ANOVA p-value bellow the cutoff will be considered
+#' for scoring.
+#' @param correlation_cutoff a numeric value that specifies the correlation cutoff used for data
+#' filtering. Any fits with a correlation above the cutoff will be considered for scoring.
 #' @param log_logarithmic a logical value that indicates if a logarithmic or log-logarithmic model
 #' is fitted. If response values form a symmetric curve for non-log transformed dose values, a
 #' logarithmic model instead of a log-logarithmic model should be used. Usually biological dose
@@ -135,6 +148,7 @@ parallel_fit_drc_4p <- function(data,
                                 filter = "post",
                                 replicate_completeness = 0.7,
                                 condition_completeness = 0.5,
+                                anova_cutoff = 0.05,
                                 correlation_cutoff = 0.8,
                                 log_logarithmic = TRUE,
                                 retain_columns = NULL,
@@ -163,12 +177,6 @@ parallel_fit_drc_4p <- function(data,
       return(invisible(NULL))
     }
   }
-  if (filter == "pre") {
-    stop(strwrap('"pre" cannot be selected as a filter option for parallel
-fitting. Use "post" or use the fit_drc_4p function.',
-      prefix = "\n", initial = ""
-    ))
-  }
   . <- NULL
   terminate <- FALSE
 
@@ -209,6 +217,7 @@ fitting. Use "post" or use the fit_drc_4p function.',
       filter = filter,
       replicate_completeness = replicate_completeness,
       condition_completeness = condition_completeness,
+      anova_cutoff = anova_cutoff,
       correlation_cutoff = correlation_cutoff,
       log_logarithmic = log_logarithmic,
       retain_columns = {{ retain_columns }},
@@ -226,24 +235,26 @@ fitting. Use "post" or use the fit_drc_4p function.',
   result <- result %>%
     dplyr::arrange(desc(.data$correlation))
 
-  if (filter == "post") {
+  if (filter != "none") {
     result <- result %>%
       dplyr::mutate(anova_adj_pval = stats::p.adjust(.data$anova_pval, method = "BH")) %>%
-      dplyr::mutate(anova_significant = ifelse(.data$anova_adj_pval > 0.05 | is.na(.data$anova_adj_pval),
+      dplyr::mutate(anova_significant = ifelse(.data$anova_adj_pval > anova_cutoff | is.na(.data$anova_adj_pval),
         FALSE,
         TRUE
       )) %>%
-      dplyr::mutate(passed_filter = ((.data$enough_conditions == TRUE &
+      dplyr::mutate(passed_filter = (((.data$enough_conditions == TRUE &
         .data$anova_significant == TRUE) |
         .data$dose_MNAR == TRUE) &
         .data$correlation >= correlation_cutoff &
-        .data$min_model < .data$max_model) %>%
+        .data$min_model < .data$max_model) |
+          .data$dose_MNAR) %>%
       dplyr::group_by(.data$passed_filter) %>%
-      dplyr::mutate(score = ifelse(.data$passed_filter,
+      dplyr::mutate(score = ifelse(.data$passed_filter & !.data$dose_MNAR,
         (scale_protti(-log10(.data$anova_pval), method = "01") + scale_protti(.data$correlation, method = "01")) / 2,
         NA
       )) %>%
       dplyr::ungroup() %>%
+      dplyr::mutate(score = ifelse(is.na(.data$score) & .data$passed_filter == TRUE, 0, .data$score)) %>%
       dplyr::arrange(dplyr::desc(.data$correlation)) %>%
       dplyr::arrange(dplyr::desc(.data$score)) %>%
       dplyr::select(-.data$rank) %>%
diff --git a/man/fit_drc_4p.Rd b/man/fit_drc_4p.Rd
index 0397d7a0..a4213e50 100644
--- a/man/fit_drc_4p.Rd
+++ b/man/fit_drc_4p.Rd
@@ -13,6 +13,7 @@ fit_drc_4p(
   filter = "post",
   replicate_completeness = 0.7,
   condition_completeness = 0.5,
+  anova_cutoff = 0.05,
   correlation_cutoff = 0.8,
   log_logarithmic = TRUE,
   include_models = FALSE,
@@ -33,17 +34,15 @@ values, e.g. log2 transformed intensities.}
 \item{dose}{a numeric column in the \code{data} data frame that contains the dose values, e.g.
 the treatment concentrations.}
 
-\item{filter}{a character value that determines if models should be filtered and if they should
-be filtered before or after the curve fits. Filtering of models can be skipped with
-\code{filter = "none"}. Data can be filtered prior to model fitting with \code{filter = "pre"}.
-In that case models will only be fitted for data that passed the filtering step. This will
-allow for faster model fitting since only fewer models will be fit. If you plan on performing
-an enrichment analysis you have to choose \code{filter = "post"}. All models will be fit (even
-the ones that do not pass the filtering criteria). For enrichment analysis you should use both
-good (i.e. models that pass the filtering) and bad (i.e. models that do not pass the filtering)
-models. Therefore, for post-filtering the full list is returned and it will only contain
-annotations that indicate (\code{passed_filter}) if the filtering was passed or not. Default is
-"post". For ANOVA an adjusted p-value of 0.05 is used as a cutoff.}
+\item{filter}{a character value that can either be \code{"pre"}, \code{"post"} or \code{"none"}. The data is
+annotated for completeness, ANOVA significance and the completeness distribution along
+the doses (\code{"pre"} and \code{"post"}). The combined output of this filtering step can be found in
+the \code{passed_filter} column and depends on the cutoffs provided to the function. Note that this
+is only an annotation and nothing is removed from the output. If \code{"pre"} is selected then, in
+addition to the annotation, the data is filtered for completeness based on the condition completeness
+prior to the curve fitting and ANOVA calculation and p-value adjustment. This has the benefit that less
+curves need to be fitted and that the ANOVA p-value adjustment is done only on the relevant set of tests.
+If \code{"none"} is selected the data will be neither annotated nor filtered.}
 
 \item{replicate_completeness}{a numeric value which similar to \code{completenss_MAR} of the
 \code{assign_missingness} function sets a threshold for the completeness of data. In contrast
@@ -61,8 +60,12 @@ the number of conditions and then adjusted downward. The resulting number is the
 of conditions that need to fulfill the \code{replicate_completeness} argument for a peptide to
 pass the filtering.}
 
-\item{correlation_cutoff}{a numeric vector that specifies the correlation cutoff used for data
-filtering.}
+\item{anova_cutoff}{a numeric value that specifies the ANOVA adjusted p-value cutoff used for
+data filtering. Any fits with an adjusted ANOVA p-value bellow the cutoff will be considered
+for scoring.}
+
+\item{correlation_cutoff}{a numeric value that specifies the correlation cutoff used for data
+filtering. Any fits with a correlation above the cutoff will be considered for scoring.}
 
 \item{log_logarithmic}{a logical value that indicates if a logarithmic or log-logarithmic model
 is fitted. If response values form a symmetric curve for non-log transformed dose values, a
@@ -91,42 +94,50 @@ is returned in the columns \code{plot_curve} (curve and confidence interval) and
 }
 \description{
 Function for fitting four-parameter dose response curves for each group (precursor, peptide or
-protein). In addition it can filter data based on completeness, the completeness distribution
-and statistical testing using ANOVA.
+protein). In addition it can annotate data based on completeness, the completeness distribution
+and statistical testing using ANOVA. Filtering by the function is only performed based on completeness
+if selected.
 }
 \details{
-If data filtering options are selected, data is filtered based on multiple criteria.
-In general, curves are only fitted if there are at least 5 conditions with data points present
-to ensure that there is potential for a good curve fit. Therefore, this is also the case if no
-filtering option is selected. Furthermore, a completeness cutoff is defined for filtering. By
-default each entity (e.g. precursor) is filtered to contain at least 70\% of total replicates
-(adjusted downward) for at least 50\% of all conditions (adjusted downward). This can be adjusted
-with the according arguments. In addition to the completeness cutoff, also a significance cutoff
-is applied. ANOVA is used to compute the statistical significance of the change for each entity.
-The resulting p-value is adjusted using the Benjamini-Hochberg method and a cutoff of q <= 0.05
-is applied. Curve fits that have a minimal value that is higher than the maximal value are
-excluded as they were likely wrongly fitted. Curves with a correlation below 0.8 are not passing
-the filtering. If a fit does not fulfill the significance or completeness cutoff, it has a chance
-to still be considered if half of its values (+/-1 value) pass the replicate completeness
-criteria and half do not pass it. In order to fall into this category, the values that fulfill t
-he completeness cutoff and the ones that do not fulfill it need to be consecutive, meaning
-located next to each other based on their concentration values. Furthermore, the values that
-do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
+If data filtering options are selected, data is annotated based on multiple criteria.
+If \code{"post"} is selected the data is annotated based on completeness, the completeness distribution, the
+adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on
+the \code{replicate_completeness} and \code{condition_completeness} arguments. The completeness distribution determines
+if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a
+features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into
+this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it
+need to be consecutive, meaning located next to each other based on their concentration values. Furthermore,
+the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
 between the two groups is tested for statistical significance using a Welch's t-test and a
-cutoff of p <= 0.1 (we want to mainly discard curves that falsly fit the other criteria but that
+cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that
 have clearly non-significant differences in mean). This allows curves to be considered that have
 missing values in half of their observations due to a decrease in intensity. It can be thought
 of as conditions that are missing not at random (MNAR). It is often the case that those entities
 do not have a significant p-value since half of their conditions are not considered due to data
-missingness.
-
-The final filtered list is ranked based on a score calculated on entities that pass the filter.
+missingness. The ANOVA test is performed on the features by concentration. If it is significant it is
+likely that there is some response. However, this test would also be significant even if there is one
+outlier concentration so it should only be used only in combination with other cutoffs to determine
+if a feature is significant. The \code{passed_filter} column is \code{TRUE} for all the
+features that pass the above mentioned criteria and that have a correlation greater than the cutoff
+(default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).
+
+The final list is ranked based on a score calculated on entities that pass the filter.
 The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and the
 correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an
 entity can have is 1 with both the highest correlation and adjusted p-value. The rank is
 corresponding to this score. Please note, that entities with MNAR conditions might have a
-lower score due to the missing or non-significant ANOVA p-value. You should have a look at
-curves that are TRUE for \code{dose_MNAR} in more detail.
+lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated
+the usual way these cases receive a score of 0. You should have a look at curves that are TRUE
+for \code{dose_MNAR} in more detail.
+
+If the \code{"pre"} option is selected for the \code{filter} argument then the data is filtered for completeness
+prior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.
+We recommend the \code{"pre"} option because it leaves you with not only the likely hits of your treatment, but
+also with rather high confidence true negative results. This is because the filtered data has a high
+degree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.
+
+Please note that in general, curves are only fitted if there are at least 5 conditions with data points present
+to ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.
 }
 \examples{
 \donttest{
diff --git a/man/parallel_fit_drc_4p.Rd b/man/parallel_fit_drc_4p.Rd
index 5d9a6dc0..b52385a7 100644
--- a/man/parallel_fit_drc_4p.Rd
+++ b/man/parallel_fit_drc_4p.Rd
@@ -13,6 +13,7 @@ parallel_fit_drc_4p(
   filter = "post",
   replicate_completeness = 0.7,
   condition_completeness = 0.5,
+  anova_cutoff = 0.05,
   correlation_cutoff = 0.8,
   log_logarithmic = TRUE,
   retain_columns = NULL,
@@ -33,12 +34,15 @@ values, e.g. log2 transformed intensities.}
 \item{dose}{a numeric column in the \code{data} data frame that contains the dose values, e.g.
 the treatment concentrations.}
 
-\item{filter}{a character value that determines if models should be filtered and if they should
-be filtered. The option \code{"pre"} is not available for parallel fitting of models. This is
-because ANOVA adjusted p-values would be wrongly calculated because the dataset is split onto
-multiple cores. Default is "post" and we recommend always using "post" because compared to
-"none" only some additional columns are added that contain the filter information. For ANOVA
-an adjusted p-value of 0.05 is used as a cutoff.}
+\item{filter}{a character value that can either be \code{"pre"}, \code{"post"} or \code{"none"}. The data is
+annotated for completeness, ANOVA significance and the completeness distribution along
+the doses (\code{"pre"} and \code{"post"}). The combined output of this filtering step can be found in
+the \code{passed_filter} column and depends on the cutoffs provided to the function. Note that this
+is only an annotation and nothing is removed from the output. If \code{"pre"} is selected then, in
+addition to the annotation, the data is filtered for completeness based on the condition completeness
+prior to the curve fitting and ANOVA calculation and p-value adjustment. This has the benefit that less
+curves need to be fitted and that the ANOVA p-value adjustment is done only on the relevant set of tests.
+If \code{"none"} is selected the data will be neither annotated nor filtered.}
 
 \item{replicate_completeness}{a numeric value which similar to \code{completenss_MAR} of the
 \code{assign_missingness} function sets a threshold for the completeness of data. In contrast
@@ -56,8 +60,12 @@ the number of conditions and then adjusted downward. The resulting number is the
 of conditions that need to fulfill the \code{replicate_completeness} argument for a peptide to
 pass the filtering.}
 
-\item{correlation_cutoff}{a numeric vector that specifies the correlation cutoff used for data
-filtering.}
+\item{anova_cutoff}{a numeric value that specifies the ANOVA adjusted p-value cutoff used for
+data filtering. Any fits with an adjusted ANOVA p-value bellow the cutoff will be considered
+for scoring.}
+
+\item{correlation_cutoff}{a numeric value that specifies the correlation cutoff used for data
+filtering. Any fits with a correlation above the cutoff will be considered for scoring.}
 
 \item{log_logarithmic}{a logical value that indicates if a logarithmic or log-logarithmic model
 is fitted. If response values form a symmetric curve for non-log transformed dose values, a
@@ -90,38 +98,45 @@ individual fit objects when using this function as compared to the non parallel
 they are too large for efficient export from the workers.
 }
 \details{
-If data filtering options are selected, data is filtered based on multiple criteria.
-In general, curves are only fitted if there are at least 5 conditions with data points present
-to ensure that there is potential for a good curve fit. Therefore, this is also the case if no
-filtering option is selected. Furthermore, a completeness cutoff is defined for filtering. By
-default each entity (e.g. precursor) is filtered to contain at least 70\% of total replicates
-(adjusted downward) for at least 50\% of all conditions (adjusted downward). This can be adjusted
-with the according arguments. In addition to the completeness cutoff, also a significance cutoff
-is applied. ANOVA is used to compute the statistical significance of the change for each entity.
-The resulting p-value is adjusted using the Benjamini-Hochberg method and a cutoff of q <= 0.05
-is applied. Curve fits that have a minimal value that is higher than the maximal value are
-excluded as they were likely wrongly fitted. Curves with a correlation below 0.8 are not passing
-the filtering. If a fit does not fulfill the significance or completeness cutoff, it has a
-chance to still be considered if half of its values (+/-1 value) pass the replicate completeness
-criteria and half do not pass it. In order to fall into this category, the values that fulfill
-the completeness cutoff and the ones that do not fulfill it need to be consecutive, meaning
-located next to each other based on their concentration values. Furthermore, the values that
-do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
+If data filtering options are selected, data is annotated based on multiple criteria.
+If \code{"post"} is selected the data is annotated based on completeness, the completeness distribution, the
+adjusted ANOVA p-value cutoff and a correlation cutoff. Completeness of features is determined based on
+the \code{replicate_completeness} and \code{condition_completeness} arguments. The completeness distribution determines
+if there is a distribution of not random missingness of data along the dose. For this it is checked if half of a
+features values (+/-1 value) pass the replicate completeness criteria and half do not pass it. In order to fall into
+this category, the values that fulfill the completeness cutoff and the ones that do not fulfill it
+need to be consecutive, meaning located next to each other based on their concentration values. Furthermore,
+the values that do not pass the completeness cutoff need to be lower in intensity. Lastly, the difference
 between the two groups is tested for statistical significance using a Welch's t-test and a
-cutoff of p <= 0.1 (we want to mainly discard curves that falsly fit the other criteria
-but that have clearly non-significant differences in mean). This allows curves to be considered
-that have missing values in half of their observations due to a decrease in intensity. It can
-be thought of as conditions that are missing not at random (MNAR). It is often the case that
-those entities do not have a significant p-value since half of their conditions are not
-considered due to data missingness.
-
-The final filtered list is ranked based on a score calculated on entities that pass the filter.
+cutoff of p <= 0.1 (we want to mainly discard curves that falsely fit the other criteria but that
+have clearly non-significant differences in mean). This allows curves to be considered that have
+missing values in half of their observations due to a decrease in intensity. It can be thought
+of as conditions that are missing not at random (MNAR). It is often the case that those entities
+do not have a significant p-value since half of their conditions are not considered due to data
+missingness. The ANOVA test is performed on the features by concentration. If it is significant it is
+likely that there is some response. However, this test would also be significant even if there is one
+outlier concentration so it should only be used only in combination with other cutoffs to determine
+if a feature is significant. The \code{passed_filter} column is \code{TRUE} for all the
+features that pass the above mentioned criteria and that have a correlation greater than the cutoff
+(default is 0.8) and the adjusted ANOVA p-value below the cutoff (default is 0.05).
+
+The final list is ranked based on a score calculated on entities that pass the filter.
 The score is the negative log10 of the adjusted ANOVA p-value scaled between 0 and 1 and the
 correlation scaled between 0 and 1 summed up and divided by 2. Thus, the highest score an
 entity can have is 1 with both the highest correlation and adjusted p-value. The rank is
 corresponding to this score. Please note, that entities with MNAR conditions might have a
-lower score due to the missing or non-significant ANOVA p-value. You should have a look at
-curves that are TRUE for \code{dose_MNAR} in more detail.
+lower score due to the missing or non-significant ANOVA p-value. If no score could be calculated
+the usual way these cases receive a score of 0. You should have a look at curves that are TRUE
+for \code{dose_MNAR} in more detail.
+
+If the \code{"pre"} option is selected for the \code{filter} argument then the data is filtered for completeness
+prior to curve fitting and the ANOVA test. Otherwise annotation is performed exactly as mentioned above.
+We recommend the \code{"pre"} option because it leaves you with not only the likely hits of your treatment, but
+also with rather high confidence true negative results. This is because the filtered data has a high
+degree of completeness making it unlikely that a real dose-response curve is missed due to data missingness.
+
+Please note that in general, curves are only fitted if there are at least 5 conditions with data points present
+to ensure that there is potential for a good curve fit. This is done independent of the selected filtering option.
 }
 \examples{
 \dontrun{
diff --git a/tests/testthat/test-workflow.R b/tests/testthat/test-workflow.R
index cab24ad4..6eb70181 100644
--- a/tests/testthat/test-workflow.R
+++ b/tests/testthat/test-workflow.R
@@ -650,7 +650,7 @@ if (Sys.getenv("TEST_PROTTI") == "true") {
   test_that("fit_drc_4p works", {
     # did not test the argument include_models = TRUE
     expect_is(drc_fit, "data.frame")
-    expect_equal(nrow(drc_fit), 282)
+    expect_equal(nrow(drc_fit), 306)
     expect_equal(ncol(drc_fit), 18)
     expect_equal(round(max(drc_fit$correlation, na.rm = TRUE), digits = 3), 0.876)
     expect_equal(round(min(drc_fit$anova_pval, na.rm = TRUE), digits = 6), 0.007297)
@@ -786,3 +786,4 @@ test_that("calculate_aa_scores works", {
   expect_equal(nrow(aa_fingerprint), 45)
   expect_equal(ncol(aa_fingerprint), 3)
 })
+