diff --git a/tests/unit_tests/iterators/test_adaptive_sampling.py b/tests/unit_tests/iterators/test_adaptive_sampling.py
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+++ b/tests/unit_tests/iterators/test_adaptive_sampling.py
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+#
+# SPDX-License-Identifier: LGPL-3.0-or-later
+# Copyright (c) 2024-2025, QUEENS contributors.
+#
+# This file is part of QUEENS.
+#
+# QUEENS is free software: you can redistribute it and/or modify it under the terms of the GNU
+# Lesser General Public License as published by the Free Software Foundation, either version 3 of
+# the License, or (at your option) any later version. QUEENS is distributed in the hope that it will
+# be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+# FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You
+# should have received a copy of the GNU Lesser General Public License along with QUEENS. If not,
+# see <https://www.gnu.org/licenses/>.
+#
+"""Unit tests for AdaptiveSampling iterator."""
+
+from unittest.mock import Mock
+
+import numpy as np
+import pytest
+
+from queens.iterators.adaptive_sampling import AdaptiveSampling
+
+
+@pytest.fixture(name="adaptive_sampling_iterator")
+def fixture_adaptive_sampling_iterator(global_settings, default_parameters_uniform_2d):
+    """Fixture for AdaptiveSampling iterator with mocked dependencies."""
+    # Mock model
+    model = Mock()
+
+    # Mock likelihood model with y_obs
+    likelihood_model = Mock()
+    likelihood_model.y_obs = np.array([1.0, 2.0, 3.0])
+
+    # Mock solving iterator
+    solving_iterator = Mock()
+
+    # Initial training samples
+    initial_train_samples = np.array([[0.1, 0.2], [0.3, 0.4]])
+
+    iterator = AdaptiveSampling(
+        model=model,
+        parameters=default_parameters_uniform_2d,
+        global_settings=global_settings,
+        likelihood_model=likelihood_model,
+        initial_train_samples=initial_train_samples,
+        solving_iterator=solving_iterator,
+        num_new_samples=2,
+        num_steps=1,
+    )
+
+    return iterator
+
+
+def test_filter_failed_evaluations_no_failures(adaptive_sampling_iterator):
+    """Test _filter_failed_evaluations when there are no failures."""
+    # Set up test data without any NaN values
+    adaptive_sampling_iterator.x_train = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
+    adaptive_sampling_iterator.y_train = np.array([[1.0], [2.0], [3.0]])
+    adaptive_sampling_iterator.model_outputs = np.array(
+        [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
+    )
+    adaptive_sampling_iterator.x_train_new = np.array([[0.5, 0.6]])
+
+    original_x_train = adaptive_sampling_iterator.x_train.copy()
+    original_y_train = adaptive_sampling_iterator.y_train.copy()
+    original_model_outputs = adaptive_sampling_iterator.model_outputs.copy()
+
+    # Run the method
+    adaptive_sampling_iterator._filter_failed_evaluations()
+
+    # Verify nothing was filtered
+    np.testing.assert_array_equal(adaptive_sampling_iterator.x_train, original_x_train)
+    np.testing.assert_array_equal(adaptive_sampling_iterator.y_train, original_y_train)
+    np.testing.assert_array_equal(adaptive_sampling_iterator.model_outputs, original_model_outputs)
+
+    # Verify no failed evaluations were stored
+    assert adaptive_sampling_iterator.x_train_failed.shape[0] == 0
+    assert adaptive_sampling_iterator.model_outputs_failed.shape[0] == 0
+
+
+def test_filter_failed_evaluations_some_failures(adaptive_sampling_iterator):
+    """Test _filter_failed_evaluations when some evaluations fail (contain NaN)."""
+    # Set up test data with some NaN values (row 1 has NaN)
+    adaptive_sampling_iterator.x_train = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
+    adaptive_sampling_iterator.y_train = np.array([[1.0], [2.0], [3.0]])
+    adaptive_sampling_iterator.model_outputs = np.array(
+        [[1.0, 2.0, 3.0], [np.nan, 5.0, 6.0], [7.0, 8.0, 9.0]]
+    )
+    adaptive_sampling_iterator.x_train_new = np.array([[0.3, 0.4], [0.5, 0.6]])
+
+    # Run the method
+    adaptive_sampling_iterator._filter_failed_evaluations()
+
+    # Verify successful evaluations remain (rows 0 and 2)
+    expected_x_train = np.array([[0.1, 0.2], [0.5, 0.6]])
+    expected_y_train = np.array([[1.0], [3.0]])
+    expected_model_outputs = np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]])
+
+    np.testing.assert_array_equal(adaptive_sampling_iterator.x_train, expected_x_train)
+    np.testing.assert_array_equal(adaptive_sampling_iterator.y_train, expected_y_train)
+    np.testing.assert_array_equal(adaptive_sampling_iterator.model_outputs, expected_model_outputs)
+
+    # Verify failed evaluations were stored (row 1)
+    expected_x_train_failed = np.array([[0.3, 0.4]])
+    expected_model_outputs_failed = np.array([[np.nan, 5.0, 6.0]])
+
+    np.testing.assert_array_equal(
+        adaptive_sampling_iterator.x_train_failed, expected_x_train_failed
+    )
+    np.testing.assert_array_equal(
+        adaptive_sampling_iterator.model_outputs_failed, expected_model_outputs_failed
+    )
+
+
+def test_filter_failed_evaluations_all_failures(adaptive_sampling_iterator):
+    """Test _filter_failed_evaluations when all evaluations fail."""
+    # Set up test data with all NaN values
+    adaptive_sampling_iterator.x_train = np.array([[0.1, 0.2], [0.3, 0.4]])
+    adaptive_sampling_iterator.y_train = np.array([[1.0], [2.0]])
+    adaptive_sampling_iterator.model_outputs = np.array(
+        [[np.nan, 2.0, 3.0], [4.0, np.nan, 6.0]]
+    )
+    adaptive_sampling_iterator.x_train_new = np.array([[0.1, 0.2], [0.3, 0.4]])
+
+    # Run the method
+    adaptive_sampling_iterator._filter_failed_evaluations()
+
+    # Verify all evaluations were filtered out
+    assert adaptive_sampling_iterator.x_train.shape[0] == 0
+    assert adaptive_sampling_iterator.y_train.shape[0] == 0
+    assert adaptive_sampling_iterator.model_outputs.shape[0] == 0
+
+    # Verify all failed evaluations were stored
+    expected_x_train_failed = np.array([[0.1, 0.2], [0.3, 0.4]])
+    np.testing.assert_array_equal(
+        adaptive_sampling_iterator.x_train_failed, expected_x_train_failed
+    )
+    assert adaptive_sampling_iterator.model_outputs_failed.shape[0] == 2
+
+
+def test_filter_failed_evaluations_multiple_calls(adaptive_sampling_iterator):
+    """Test _filter_failed_evaluations accumulates failed evaluations across calls."""
+    # First call with one failure
+    adaptive_sampling_iterator.x_train = np.array([[0.1, 0.2], [0.3, 0.4]])
+    adaptive_sampling_iterator.y_train = np.array([[1.0], [2.0]])
+    adaptive_sampling_iterator.model_outputs = np.array([[np.nan, 2.0, 3.0], [4.0, 5.0, 6.0]])
+    adaptive_sampling_iterator.x_train_new = np.array([[0.1, 0.2], [0.3, 0.4]])
+
+    adaptive_sampling_iterator._filter_failed_evaluations()
+
+    # Verify first filtering
+    assert adaptive_sampling_iterator.x_train_failed.shape[0] == 1
+    np.testing.assert_array_equal(adaptive_sampling_iterator.x_train_failed, [[0.1, 0.2]])
+
+    # Second call with another failure
+    adaptive_sampling_iterator.x_train = np.array([[0.3, 0.4], [0.5, 0.6]])
+    adaptive_sampling_iterator.y_train = np.array([[2.0], [3.0]])
+    adaptive_sampling_iterator.model_outputs = np.array([[4.0, 5.0, 6.0], [7.0, np.nan, 9.0]])
+    adaptive_sampling_iterator.x_train_new = np.array([[0.5, 0.6]])
+
+    adaptive_sampling_iterator._filter_failed_evaluations()
+
+    # Verify failed evaluations were accumulated
+    assert adaptive_sampling_iterator.x_train_failed.shape[0] == 2
+    expected_x_train_failed = np.array([[0.1, 0.2], [0.5, 0.6]])
+    np.testing.assert_array_equal(
+        adaptive_sampling_iterator.x_train_failed, expected_x_train_failed
+    )