In this paper, we describe precedent-based explanations for case-based classification systems. Previous work has shown that explanation cases that are more marginal than the query case, in the sense of lying between the query case and the decision boundary, are more con- vincing explanations. We show how to retrieve such explanation cases in a way that requires lower knowl- edge engineering overheads than previously. We eval- uate our approaches empirically, finding that the expla- nations that our systems retrieve are often more con- vincing than those found by the previous approach. The paper ends with a thorough discussion of a range of fac- tors that affect precedent-based explanations, many of which warrant further research.