diff --git a/openadmet/models/active_learning/committee.py b/openadmet/models/active_learning/committee.py
index 5402a70e..d07d7dca 100644
--- a/openadmet/models/active_learning/committee.py
+++ b/openadmet/models/active_learning/committee.py
@@ -353,7 +353,7 @@ def query(self, X, query_strategy: str = None, **kwargs):
 
         return _ACQUISITION_FUNCTIONS[query_strategy](mean, std, **kwargs)
 
-    def _predict(self, X, return_std=False, **kwargs):
+    def _predict(self, X, return_std=False, return_all=False, **kwargs):
         """
         Make predictions using the committee model.
 
@@ -363,35 +363,46 @@ def _predict(self, X, return_std=False, **kwargs):
             The input samples to predict.
         return_std : bool, optional
             Whether to return the standard deviation of the predictions.
+            Mutually exclusive with ``return_all``.
+        return_all : bool, optional
+            Whether to return the raw per-member predictions of shape
+            (n_samples, n_tasks, n_members) instead of the mean (and std).
+            Mutually exclusive with ``return_std``.
         **kwargs : dict
             Additional keyword arguments to pass to the committee's predict method.
 
         Returns
         -------
-        array-like
-            Predicted values or probabilities, depending on the committee's implementation.
+        array-like or tuple
+            mean, or (mean, std), or ndarray of shape (n_samples, n_tasks, n_members)
+            depending on the values of return_std and return_all.
 
         """
-        # Make predictions
+        if return_std and return_all:
+            raise ValueError(
+                "return_std and return_all are mutually exclusive. "
+                "When return_all=True, compute mean and std from the returned array as needed."
+            )
+
+        # Make predictions: (n_samples, n_tasks, n_members)
         preds = np.stack([model.predict(X, **kwargs) for model in self.models], axis=-1)
 
+        if return_all:
+            return preds
+
         # Compute mean
         mean = np.mean(preds, axis=-1)
 
-        # Skip std if not requested
-        if return_std is False:
+        if not return_std:
             return mean
 
         # Compute standard deviation, guard against zero std
         std = np.maximum(np.std(preds, axis=-1), 1e-8)
-
-        # Calibrate std if calibration model is available
         if self.calibrated:
             std = self._get_calibration_function()(std)
-
         return mean, std
 
-    def predict(self, X, return_std=False, **kwargs):
+    def predict(self, X, return_std=False, return_all=False, **kwargs):
         """
         Make predictions using the committee model.
 
@@ -401,13 +412,19 @@ def predict(self, X, return_std=False, **kwargs):
             The input samples to predict.
         return_std : bool, optional
             Whether to return the standard deviation of the predictions.
+            Mutually exclusive with ``return_all``.
+        return_all : bool, optional
+            Whether to return the raw per-member predictions of shape
+            (n_samples, n_tasks, n_members) instead of the mean (and std).
+            Mutually exclusive with ``return_std``.
         **kwargs : dict
             Additional keyword arguments to pass to the committee's predict method.
 
         Returns
         -------
-        array-like
-            Predicted values or probabilities, depending on the committee's implementation.
+        array-like or tuple
+            mean, or (mean, std), or ndarray of shape (n_samples, n_tasks, n_members)
+            depending on the values of return_std and return_all.
 
         """
         if return_std is True and not self.calibrated:
@@ -415,7 +432,7 @@ def predict(self, X, return_std=False, **kwargs):
                 "Standard deviation not calibrated: consider calling `calibrate_uncertainty`."
             )
 
-        return self._predict(X, return_std=return_std, **kwargs)
+        return self._predict(X, return_std=return_std, return_all=return_all, **kwargs)
 
     def _save_calibration_model(self, path: PathLike = "calibration_model.pkl"):
         # Save calibration model
diff --git a/openadmet/models/tests/unit/active_learning/test_active_learning.py b/openadmet/models/tests/unit/active_learning/test_active_learning.py
index f560c967..a6593204 100644
--- a/openadmet/models/tests/unit/active_learning/test_active_learning.py
+++ b/openadmet/models/tests/unit/active_learning/test_active_learning.py
@@ -36,6 +36,31 @@ def dummy_models():
     return models
 
 
+def test_return_all(toy_data):
+    """Test that return_all exposes raw per-member predictions with correct shape."""
+    X_train, _, X_test, y_train, _, _ = toy_data
+    n_members = 4
+    n_tasks = 1
+
+    committee = CommitteeRegressor.train(
+        X_train,
+        y_train,
+        mod_class=DummyRegressorModel,
+        n_models=n_members,
+    )
+
+    # return_all returns only the raw array
+    preds = committee.predict(X_test, return_all=True)
+    assert preds.shape == (X_test.shape[0], n_tasks, n_members)
+    # mean and std are derivable from the returned array
+    assert np.mean(preds, axis=-1).shape == (X_test.shape[0], n_tasks)
+    assert np.std(preds, axis=-1).shape == (X_test.shape[0], n_tasks)
+
+    # return_all and return_std are mutually exclusive
+    with pytest.raises(ValueError, match="mutually exclusive"):
+        committee.predict(X_test, return_std=True, return_all=True)
+
+
 @pytest.fixture
 def trained_committee(dummy_models, toy_data):
     """