diff --git a/src/amltk/optimization/optimizers/neps.py b/src/amltk/optimization/optimizers/neps.py
index 4f2a4172..10265993 100644
--- a/src/amltk/optimization/optimizers/neps.py
+++ b/src/amltk/optimization/optimizers/neps.py
@@ -40,7 +40,7 @@
 
 The below example shows how you can use neps to optimize an sklearn pipeline.
 
-```python exec="False" source="material-block" result="python"
+```python
 from __future__ import annotations
 
 import logging
@@ -52,7 +52,7 @@
 
 from amltk.optimization.optimizers.neps import NEPSOptimizer
 from amltk.scheduling import Scheduler
-from amltk.optimization import History, Trial
+from amltk.optimization import History, Trial, Metric
 from amltk.pipeline import Component
 
 logging.basicConfig(level=logging.INFO)
@@ -73,10 +73,10 @@ def target_function(trial: Trial, pipeline: Pipeline) -> Trial.Report:
     return trial.fail()
 from amltk._doc import make_picklable; make_picklable(target_function)  # markdown-exec: hide
 
-
 pipeline = Component(RandomForestClassifier, space={"n_estimators": (10, 100)})
-space = pipeline.search_space(parser=NEPSOptimizer.preferred_parser())
-optimizer = NEPSOptimizer.create(space=space)
+
+metric = Metric("accuracy", minimize=False, bounds=(0, 1))
+optimizer = NEPSOptimizer.create(space=pipeline, metrics=metric, bucket="neps-doc-example")
 
 N_WORKERS = 2
 scheduler = Scheduler.with_processes(N_WORKERS)
@@ -103,6 +103,7 @@ def add_to_history(_, report: Trial.Report):
 scheduler.run(timeout=3, wait=False)
 
 print(history.df())
+optimizer.bucket.rmdir()  # markdown-exec: hide
 ```
 
 !!! todo "Deep Learning"
@@ -112,6 +113,7 @@ def add_to_history(_, report: Trial.Report):
 !!! todo "Graph Search Spaces"
 
     Write an example demonstrating NEPS with its graph search spaces
+
 """  # noqa: E501
 from __future__ import annotations
 
diff --git a/src/amltk/optimization/optimizers/optuna.py b/src/amltk/optimization/optimizers/optuna.py
index 54a50f98..81015f0a 100644
--- a/src/amltk/optimization/optimizers/optuna.py
+++ b/src/amltk/optimization/optimizers/optuna.py
@@ -60,10 +60,10 @@ def target_function(trial: Trial, pipeline: Pipeline) -> Trial.Report:
     return trial.fail()
 from amltk._doc import make_picklable; make_picklable(target_function)  # markdown-exec: hide
 
+pipeline = Component(RandomForestClassifier, space={"n_estimators": (10, 100)})
 
 accuracy_metric = Metric("accuracy", minimize=False, bounds=(0, 1))
-pipeline = Component(RandomForestClassifier, space={"n_estimators": (10, 100)})
-optimizer = OptunaOptimizer.create(space=pipeline, metrics=accuracy_metric)
+optimizer = OptunaOptimizer.create(space=pipeline, metrics=accuracy_metric, bucket="optuna-doc-example")
 
 N_WORKERS = 2
 scheduler = Scheduler.with_processes(N_WORKERS)
diff --git a/src/amltk/optimization/optimizers/smac.py b/src/amltk/optimization/optimizers/smac.py
index 64ca6d90..e28b8043 100644
--- a/src/amltk/optimization/optimizers/smac.py
+++ b/src/amltk/optimization/optimizers/smac.py
@@ -35,7 +35,7 @@
 
 from amltk.optimization.optimizers.smac import SMACOptimizer
 from amltk.scheduling import Scheduler
-from amltk.optimization import History, Trial
+from amltk.optimization import History, Trial, Metric
 from amltk.pipeline import Component, Node
 
 logging.basicConfig(level=logging.INFO)
@@ -50,16 +50,15 @@ def target_function(trial: Trial, pipeline: Node) -> Trial.Report:
         clf.fit(X_train, y_train)
         y_pred = clf.predict(X_test)
         accuracy = accuracy_score(y_test, y_pred)
-        loss = 1 - accuracy
-        return trial.success(loss=loss, accuracy=accuracy)
+        return trial.success(accuracy=accuracy)
 
     return trial.fail()
 from amltk._doc import make_picklable; make_picklable(target_function)  # markdown-exec: hide
 
+pipeline = Component(RandomForestClassifier, space={"n_estimators": (10, 100), "max_samples": (0.1, 0.9)})
 
-pipeline = Component(RandomForestClassifier, space={"n_estimators": (10, 100)})
-space = pipeline.search_space(parser=SMACOptimizer.preferred_parser())
-optimizer = SMACOptimizer.create(space=space)
+metric = Metric("accuracy", minimize=False, bounds=(0, 1))
+optimizer = SMACOptimizer.create(space=pipeline, metrics=metric, bucket="smac-doc-example")
 
 N_WORKERS = 2
 scheduler = Scheduler.with_processes(N_WORKERS)
@@ -86,6 +85,8 @@ def add_to_history(_, report: Trial.Report):
 scheduler.run(timeout=3, wait=False)
 
 print(history.df())
+optimizer.bucket.rmdir()  # markdown-exec: hide
+```
 """  # noqa: E501
 from __future__ import annotations
 
diff --git a/src/amltk/pipeline/node.py b/src/amltk/pipeline/node.py
index e6a72c68..b77d91d5 100644
--- a/src/amltk/pipeline/node.py
+++ b/src/amltk/pipeline/node.py
@@ -541,7 +541,24 @@ def search_space(
         *parser_args: P.args,
         **parser_kwargs: P.kwargs,
     ) -> ParserOutput | ConfigurationSpace | OptunaSearchSpace:
-        """Get the search space for this node."""
+        """Get the search space for this node.
+
+        Args:
+            parser: The parser to use. This can be a function that takes in
+                the node and returns the search space or a string that is one of:
+
+                * `#!python "configspace"`: Build a
+                    [`ConfigSpace.ConfigurationSpace`](https://automl.github.io/ConfigSpace/master/)
+                    out of this node.
+                * `#!python "optuna"`: Build a dict of hyperparameters that Optuna can
+                    use in its [ask and tell methods](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html#define-and-run)
+
+            parser_args: The positional arguments to pass to the parser
+            parser_kwargs: The keyword arguments to pass to the parser
+
+        Returns:
+            The search space
+        """
         match parser:
             case "configspace":
                 from amltk.pipeline.parsers.configspace import parser as cs_parser