We examine the role of similarity in ad hoc rule detection and show how previous methods can be made more corpus independent and more generally applicable. Specifically, we show that the similarity of a rule to others in the grammar is a crucial factor in determining the reliability of a rule, providing information unavailable in frequency. We also include a way to score rules which are not in the training data, thereby providing a platform for grammar generalization.