diff --git a/openml/setups/functions.py b/openml/setups/functions.py
index 7e7c296f8..fb58dc1ab 100644
--- a/openml/setups/functions.py
+++ b/openml/setups/functions.py
@@ -20,7 +20,9 @@ def setup_exists(flow, model=None):
     ----------
 
     flow : flow
-        The openml flow object.
+        The openml flow object. Should have flow id present for the main flow
+        and all subflows (i.e., it should be downloaded from the server by
+        means of flow.get, and not instantiated locally)
 
     sklearn_model : BaseEstimator, optional
         If given, the parameters are parsed from this model instead of the
@@ -36,11 +38,16 @@ def setup_exists(flow, model=None):
     openml.flows.functions._check_flow_for_server_id(flow)
 
     if model is None:
+        # model is left empty. We take the model from the flow.
         model = flow.model
-    else:
-        exists = flow_exists(flow.name, flow.external_version)
-        if exists != flow.flow_id:
-            raise ValueError('This should not happen!')
+        if flow.model is None:
+            raise ValueError('Could not locate model (neither given as'
+                             'argument nor available as flow.model)')
+
+    # checks whether the flow exists on the server and flow ids align
+    exists = flow_exists(flow.name, flow.external_version)
+    if exists != flow.flow_id:
+        raise ValueError('This should not happen!')
 
     openml_param_settings = openml.runs.OpenMLRun._parse_parameters(flow, model)
     description = xmltodict.unparse(_to_dict(flow.flow_id,
diff --git a/tests/test_runs/test_run_functions.py b/tests/test_runs/test_run_functions.py
index f622ea269..6fabac8d9 100644
--- a/tests/test_runs/test_run_functions.py
+++ b/tests/test_runs/test_run_functions.py
@@ -596,22 +596,21 @@ def test_get_run_trace(self):
 
         task = openml.tasks.get_task(task_id)
         # IMPORTANT! Do not sentinel this flow. is faster if we don't wait on openml server
-        clf = RandomizedSearchCV(RandomForestClassifier(random_state=42),
+        clf = RandomizedSearchCV(RandomForestClassifier(random_state=42,
+                                                        n_estimators=5),
                                  {"max_depth": [3, None],
                                   "max_features": [1, 2, 3, 4],
                                   "bootstrap": [True, False],
                                   "criterion": ["gini", "entropy"]},
-                                 num_iterations, random_state=42)
+                                 num_iterations, random_state=42, cv=3)
 
         # [SPEED] make unit test faster by exploiting run information from the past
         try:
             # in case the run did not exists yet
-            run = openml.runs.run_model_on_task(task, clf, avoid_duplicate_runs=True)
-            trace = openml.runs.functions._create_trace_from_arff(
-                run._generate_trace_arff_dict()
-            )
+            run = openml.runs.run_model_on_task(clf, task,
+                                                avoid_duplicate_runs=True)
             self.assertEqual(
-                len(trace.trace_iterations),
+                len(run.trace.trace_iterations),
                 num_iterations * num_folds,
             )
             run = run.publish()