diff --git a/dataframely/collection/collection.py b/dataframely/collection/collection.py
index 673b7da..1576f9f 100644
--- a/dataframely/collection/collection.py
+++ b/dataframely/collection/collection.py
@@ -531,6 +531,25 @@ def filter(
         Raises:
             ValueError: If an insufficient set of input data frames is provided, i.e. if
                 any required member of this collection is missing in the input.
+
+        Example:
+
+            .. code-block:: python
+
+                # Define collection
+                class HospitalInvoiceData(dy.Collection):
+                    invoice: dy.LazyFrame[InvoiceSchema]
+                    ...
+
+                # Filter the data and cast columns to expected types
+                good, failure = HospitalInvoiceData.filter(df, cast=True)
+
+                # Inspect the reasons for the failed rows for member `invoice`
+                print(failure.invoice.counts())
+
+                # Inspect the failed rows
+                failed_df = failure.invoice.invalid()
+                print(failed_df)
         """
         cls._validate_input_keys(data)
 
diff --git a/dataframely/schema.py b/dataframely/schema.py
index 5a197cc..c457ec7 100644
--- a/dataframely/schema.py
+++ b/dataframely/schema.py
@@ -637,7 +637,8 @@ def filter(
         cast: bool = False,
         eager: bool = True,
     ) -> FilterResult[Self] | LazyFilterResult[Self]:
-        """Filter the data frame by the rules of this schema.
+        """Filter the data frame by the rules of this schema, returning `(valid,
+        failures)`.
 
         This method can be thought of as a "soft alternative" to :meth:`validate`.
         While :meth:`validate` raises an exception when a row does not adhere to the
@@ -670,6 +671,20 @@ def filter(
 
         Note:
             This method preserves the ordering of the input data frame.
+
+        Example:
+
+            .. code-block:: python
+
+                # Filter the data and cast columns to expected types
+                good, failure = HouseSchema.filter(df, cast=True)
+
+                # Inspect the reasons for the failed rows
+                print(failure.counts())
+
+                # Inspect the failed rows
+                failed_df = failure.invalid()
+                print(failed_df)
         """
         lf = df.lazy().pipe(
             match_to_schema, cls, casting=("lenient" if cast else "none")