diff --git a/hypothesis-python/src/hypothesis/stateful.py b/hypothesis-python/src/hypothesis/stateful.py
index 5a9d44db1b..0e9706b285 100644
--- a/hypothesis-python/src/hypothesis/stateful.py
+++ b/hypothesis-python/src/hypothesis/stateful.py
@@ -431,7 +431,7 @@ def runTest(self):
         return StateMachineTestCase
 
 
-@attr.s()
+@attr.s(repr=False)
 class Rule:
     targets = attr.ib()
     function = attr.ib(repr=get_pretty_function_description)
@@ -451,6 +451,11 @@ def __attrs_post_init__(self):
             self.arguments_strategies[k] = v
         self.bundles = tuple(bundles)
 
+    def __repr__(self) -> str:
+        rep = get_pretty_function_description
+        bits = [f"{k}={rep(v)}" for k, v in attr.asdict(self).items() if v]
+        return f"{self.__class__.__name__}({', '.join(bits)})"
+
 
 self_strategy = st.runner()
 
@@ -966,17 +971,16 @@ def do_draw(self, data):
 
         feature_flags = data.draw(self.enabled_rules_strategy)
 
-        # Note: The order of the filters here is actually quite important,
-        # because checking is_enabled makes choices, so increases the size of
-        # the choice sequence. This means that if we are in a case where many
-        # rules are invalid we will make a lot more choices if we ask if they
-        # are enabled before we ask if they are valid, so our test cases will
-        # be artificially large.
-        rule = data.draw(
-            st.sampled_from(self.rules)
-            .filter(self.is_valid)
-            .filter(lambda r: feature_flags.is_enabled(r.function.__name__))
-        )
+        def rule_is_enabled(r):
+            # Note: The order of the filters here is actually quite important,
+            # because checking is_enabled makes choices, so increases the size of
+            # the choice sequence. This means that if we are in a case where many
+            # rules are invalid we would make a lot more choices if we ask if they
+            # are enabled before we ask if they are valid, so our test cases would
+            # be artificially large.
+            return self.is_valid(r) and feature_flags.is_enabled(r.function.__name__)
+
+        rule = data.draw(st.sampled_from(self.rules).filter(rule_is_enabled))
 
         arguments = {}
         for k, strat in rule.arguments_strategies.items():