diff --git a/docs/tutorials/low_level.ipynb b/docs/tutorials/low_level.ipynb
index e0004a56..ff9279fa 100644
--- a/docs/tutorials/low_level.ipynb
+++ b/docs/tutorials/low_level.ipynb
@@ -295,6 +295,66 @@
     "new_series.nest.to_flat()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7308a0a5",
+   "metadata": {},
+   "source": [
+    "## Use familiar pandas masking operations through the `.nest` accessor\n",
+    "\n",
+    "A popular usage pattern within pandas is the ability to filter `DataFrames`/`Series` using boolean masks. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a0a700f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nf = generate_data(5, 5, seed=1)\n",
+    "nf[nf[\"a\"] > 0.3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21395561",
+   "metadata": {},
+   "source": [
+    "In `nested-pandas`, the ability to do this masking is contained within the `.nest` accessor, which looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "156d0d71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nf = generate_data(5, 5, seed=1)\n",
+    "nf[\"nested.flag\"] = True  # Add an extra flag column\n",
+    "nf[\"nested\"].nest[(nf[\"nested.t\"] < 5) & nf[\"nested.flag\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ff4ee06",
+   "metadata": {},
+   "source": [
+    "The result here is a new nested column, with the masking applied, which we can assign back to the `nf` `NestedFrame` if we wish."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0f8dac3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nf[\"nested\"] = nf[\"nested\"].nest[(nf[\"nested.t\"] < 5) & nf[\"nested.flag\"]]\n",
+    "nf"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "93f73bf28d48bfdc",
@@ -735,7 +795,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lsdb",
    "language": "python",
    "name": "python3"
   },
@@ -749,7 +809,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/src/nested_pandas/series/accessor.py b/src/nested_pandas/series/accessor.py
index f69d84d2..53901cda 100644
--- a/src/nested_pandas/series/accessor.py
+++ b/src/nested_pandas/series/accessor.py
@@ -10,6 +10,7 @@
 from numpy.typing import ArrayLike
 from pandas.api.extensions import register_series_accessor
 
+from nested_pandas.nestedframe.core import NestedFrame
 from nested_pandas.series.dtype import NestedDtype
 from nested_pandas.series.packer import pack_flat, pack_sorted_df_into_struct
 from nested_pandas.series.utils import nested_types_mapper
@@ -479,6 +480,15 @@ def get_list_series(self, field: str) -> pd.Series:
         )
 
     def __getitem__(self, key: str | list[str]) -> pd.Series:
+        # Allow boolean masking given a Series of booleans
+        if isinstance(key, pd.Series) and pd.api.types.is_bool_dtype(key.dtype):
+            flat_df = self.to_flat()  # Use the flat representation
+            if not key.index.equals(flat_df.index):
+                raise ValueError("Boolean mask must have the same index as the flattened nested dataframe.")
+            # Apply the mask to the series, return a new NestedFrame
+            return NestedFrame(index=self._series.index).add_nested(flat_df[key], name=self._series.name)
+
+        # If the key is a single string, return the flat series for that field
         if isinstance(key, list):
             new_array = self._series.array.view_fields(key)
             return pd.Series(new_array, index=self._series.index, name=self._series.name)
diff --git a/tests/nested_pandas/series/test_accessor.py b/tests/nested_pandas/series/test_accessor.py
index 915c03c2..caefc9bf 100644
--- a/tests/nested_pandas/series/test_accessor.py
+++ b/tests/nested_pandas/series/test_accessor.py
@@ -736,6 +736,35 @@ def test___getitem___multiple_fields():
     )
 
 
+def test___getitem___series_masking():
+    """Test that series masking works through the accessor."""
+    nf = generate_data(5, 5, seed=1)
+    nf["nested.flag"] = True  # Add an additional boolean column
+
+    # Test a simple mask
+    result = nf["nested"].nest[nf["nested.t"] < 5.0]
+    new_nf = nf.copy()
+    new_nf["nested"] = result
+
+    expected = nf["nested"].nest.to_flat().query("t < 5.0")
+
+    assert_frame_equal(new_nf["nested"].nest.to_flat(), expected)
+
+    # Test a two column mask
+    result = nf["nested"].nest[(nf["nested.t"] < 5.0) & (nf["nested.flag"])]
+    new_nf = nf.copy()
+    new_nf["nested"] = result
+
+    expected = nf["nested"].nest.to_flat().query("t < 5.0 and flag == True")
+
+    assert_frame_equal(new_nf["nested"].nest.to_flat(), expected)
+
+    # Test for misaligned index ValueError
+    with pytest.raises(ValueError):
+        mask = nf["nested.t"] < 5.0
+        _ = nf["nested"].nest[mask[0:23]]
+
+
 def test___setitem__():
     """Test that the .nest["field"] = ... works for a single field."""
     struct_array = pa.StructArray.from_arrays(