diff --git a/tests/everest/test_scipy_backend.py b/tests/everest/test_scipy_backend.py
deleted file mode 100644
index bfef69182d2..00000000000
--- a/tests/everest/test_scipy_backend.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import pytest
-
-from ert.run_models.everest_run_model import EverestRunModel
-from everest.config import EverestConfig
-
-
-@pytest.mark.integration_test
-def test_scipy_backend(
-    copy_math_func_test_data_to_tmp, evaluator_server_config_generator
-):
-    # Arrange
-    config = EverestConfig.load_file("config_advanced.yml")
-    config.optimization.backend = "scipy"
-    config.optimization.algorithm = "SLSQP"
-    config.optimization.convergence_tolerance = 0.001
-    config.optimization.constraint_tolerance = 0.001
-    config.optimization.perturbation_num = 7
-    config.optimization.speculative = True
-    config.optimization.max_batch_num = 4
-    config.optimization.backend_options = {"maxiter": 100}
-
-    # Act
-    run_model = EverestRunModel.create(config)
-    evaluator_server_config = evaluator_server_config_generator(run_model)
-    run_model.run_experiment(evaluator_server_config)
-
-    # Assert
-    point_names = ["x-0", "x-1", "x-2"]
-    x0, x1, x2 = (run_model.result.controls["point_" + p] for p in point_names)
-    assert x0 == pytest.approx(0.1, abs=0.025)
-    assert x1 == pytest.approx(0.0, abs=0.025)
-    assert x2 == pytest.approx(0.4, abs=0.025)
-
-    # Optimal value
-    assert pytest.approx(run_model.result.total_objective, abs=0.01) == -(
-        0.25 * (1.6**2 + 1.5**2 + 0.1**2) + 0.75 * (0.4**2 + 0.5**2 + 0.1**2)
-    )
-
-    # Expected distance is the weighted average of the (squared) distances
-    #  from (x, y, z) to (-1.5, -1.5, 0.5) and (0.5, 0.5, 0.5)
-    w = config.model.realizations_weights
-    assert w == [0.25, 0.75]
-    dist_0 = (x0 + 1.5) ** 2 + (x1 + 1.5) ** 2 + (x2 - 0.5) ** 2
-    dist_1 = (x0 - 0.5) ** 2 + (x1 - 0.5) ** 2 + (x2 - 0.5) ** 2
-    expected_opt = -(w[0] * (dist_0) + w[1] * (dist_1))
-    assert expected_opt == pytest.approx(run_model.result.total_objective, abs=0.001)