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 convincing explanations. We show how to retrieve such explanation cases in a way that requires lower knowledge engineering overheads than previously. We evaluate our approaches empirically, finding that the explanations that our systems retrieve are often more convincing than those found by the previous approach. The paper ends with a thorough discussion of a range of factors that affect precedent-based explanations, many of which warrant further research.