diff --git a/CHANGELOG.md b/CHANGELOG.md
index da69aa9..531486d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- `IFP.interactions()` iterator that yields all interaction data for a given frame in
+  a single flat structure. This makes iterating over the `fp.ifp` results a bit
+  easier / less nested.
 - `Complex3D` and `fp.plot_3d` now have access to `only_interacting` and
   `remove_hydrogens` parameters to control which residues and hydrogen atoms are
   displayed. Non-polar hydrogen atoms that aren't involved in interactions are now
diff --git a/docs/notebooks/advanced.ipynb b/docs/notebooks/advanced.ipynb
index 7cc34a6..d757e33 100644
--- a/docs/notebooks/advanced.ipynb
+++ b/docs/notebooks/advanced.ipynb
@@ -381,6 +381,62 @@
    "source": [
     "You can then prepare your system and run the analysis as you normally would."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing results\n",
+    "\n",
+    "Once the fingerprint analysis has been run, there are multiple ways to access the data. The most convenient one showcased in the tutorials is through a pandas DataFrame, however this only shows the residues involved in each interaction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The complete data is stored on the `ifp` attribute of the fingerprint object as a dictionary indexed by residues:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame_number = 0\n",
+    "ligand_residue = \"UNL1\"\n",
+    "protein_residue = \"VAL200.A\"\n",
+    "\n",
+    "fp.ifp[frame_number][(ligand_residue, protein_residue)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make it easier to work with this deeply nested data structure, the results can also be accessed in a flatter structure like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for interaction_data in fp.ifp[frame_number].interactions():\n",
+    "    print(interaction_data)\n",
+    "    break"
+   ]
   }
  ],
  "metadata": {
@@ -399,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.11.6"
   },
   "orig_nbformat": 4
  },
diff --git a/prolif/ifp.py b/prolif/ifp.py
index 8754d13..48ce399 100644
--- a/prolif/ifp.py
+++ b/prolif/ifp.py
@@ -4,10 +4,18 @@
 """
 
 from collections import UserDict
+from typing import Iterator, NamedTuple
 
 from prolif.residue import ResidueId
 
 
+class InteractionData(NamedTuple):
+    ligand: ResidueId
+    protein: ResidueId
+    interaction: str
+    metadata: dict
+
+
 class IFP(UserDict):
     """Mapping between residue pairs and interaction fingerprint.
 
@@ -67,3 +75,19 @@ def __getitem__(self, key):
             "either ResidueId or residue string. If you need to filter the IFP, a "
             "single ResidueId or residue string can also be used.",
         )
+
+    def interactions(self) -> Iterator[InteractionData]:
+        """Yields all interactions data as an :class:`InteractionData` namedtuple.
+
+        .. versionadded:: 2.1.0
+
+        """
+        for (ligand_resid, protein_resid), ifp_dict in self.data.items():
+            for int_name, metadata_tuple in ifp_dict.items():
+                for metadata in metadata_tuple:
+                    yield InteractionData(
+                        ligand=ligand_resid,
+                        protein=protein_resid,
+                        interaction=int_name,
+                        metadata=metadata,
+                    )
diff --git a/tests/test_ifp.py b/tests/test_ifp.py
index 0e8d87f..0b683be 100644
--- a/tests/test_ifp.py
+++ b/tests/test_ifp.py
@@ -1,24 +1,25 @@
 import pytest
 
 from prolif.fingerprint import Fingerprint
+from prolif.ifp import IFP, InteractionData
 from prolif.residue import ResidueId
 
 
 @pytest.fixture(scope="session")
-def ifp(u, ligand_ag, protein_ag):
+def ifp(u, ligand_ag, protein_ag) -> IFP:
     fp = Fingerprint(["Hydrophobic", "VdWContact"])
     fp.run(u.trajectory[0:1], ligand_ag, protein_ag)
     return fp.ifp[0]
 
 
-def test_ifp_indexing(ifp):
+def test_ifp_indexing(ifp: IFP) -> None:
     lig_id, prot_id = "LIG1.G", "LEU126.A"
     metadata1 = ifp[(ResidueId.from_string(lig_id), ResidueId.from_string(prot_id))]
     metadata2 = ifp[(lig_id, prot_id)]
     assert metadata1 is metadata2
 
 
-def test_ifp_filtering(ifp):
+def test_ifp_filtering(ifp: IFP) -> None:
     lig_id, prot_id = "LIG1.G", "LEU126.A"
     assert ifp[lig_id] == ifp
     assert (
@@ -27,6 +28,17 @@ def test_ifp_filtering(ifp):
     )
 
 
-def test_wrong_key(ifp):
+def test_wrong_key(ifp: IFP) -> None:
     with pytest.raises(KeyError, match="does not correspond to a valid IFP key"):
         ifp[0]
+
+
+def test_interaction_data_iteration(ifp: IFP) -> None:
+    data = next(ifp.interactions())
+    assert isinstance(data, InteractionData)
+    assert data.ligand == ResidueId("LIG", 1, "G")
+    assert data.protein.chain == "A"
+    assert data.interaction in {"Hydrophobic", "VdWContact"}
+    assert "distance" in data.metadata
+    for data in ifp.interactions():
+        assert isinstance(data, InteractionData)