diff --git a/tests/test_amortizers/__init__.py b/tests/test_amortizers/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/test_amortizers/conftest.py b/tests/test_amortizers/conftest.py
new file mode 100644
index 000000000..09b0e0d4e
--- /dev/null
+++ b/tests/test_amortizers/conftest.py
@@ -0,0 +1,47 @@
+
+import keras
+import pytest
+
+import bayesflow.experimental as bf
+
+
+@pytest.fixture()
+def summary_network():
+    return None
+
+
+@pytest.fixture()
+def inference_network():
+    network = keras.Sequential([
+        keras.layers.Dense(10)
+    ])
+    network.compile(loss="mse")
+    return network
+
+
+@pytest.fixture(params=[bf.AmortizedPosterior, bf.AmortizedLikelihood])
+def amortizer(request, inference_network, summary_network):
+    Amortizer = request.param
+    return Amortizer(inference_network, summary_network)
+
+
+@pytest.fixture()
+def dataset():
+    batch_size = 16
+    batches_per_epoch = 4
+    parameter_sets = batch_size * batches_per_epoch
+    observations_per_parameter_set = 32
+
+    mean = keras.random.normal(mean=0.0, stddev=0.1, shape=(parameter_sets, 2))
+    std = keras.ops.exp(keras.random.normal(mean=0.0, stddev=0.1, shape=(parameter_sets, 2)))
+
+    mean = keras.ops.repeat(mean[:, None], observations_per_parameter_set, 1)
+    std = keras.ops.repeat(std[:, None], observations_per_parameter_set, 1)
+
+    noise = keras.random.normal(shape=(parameter_sets, observations_per_parameter_set, 2))
+
+    x = mean + std * noise
+
+    data = dict(observables=dict(x=x), parameters=dict(mean=mean, std=std))
+
+    return bf.datasets.OfflineDataset(data, batch_size=batch_size, batches_per_epoch=batches_per_epoch)
diff --git a/tests/test_amortizers/test_fit.py b/tests/test_amortizers/test_fit.py
new file mode 100644
index 000000000..7b9e84246
--- /dev/null
+++ b/tests/test_amortizers/test_fit.py
@@ -0,0 +1,11 @@
+def test_compile(amortizer):
+    amortizer.compile(optimizer="AdamW")
+
+
+def test_fit(amortizer, dataset):
+    amortizer.compile(optimizer="AdamW")
+    amortizer.fit(dataset)
+
+    assert amortizer.losses is not None
+
+