In this paper we investigate interactions between recent advances in the modeling of analogical transfer and similarity learning. Indeed, a unifying principle of case-based prediction methods was recently established, according to which the plausible inference principle of analogical transfer can be interpreted as a transfer of similarity knowledge from a situation space to an outcome space. Following this principle, the task of analogical transfer can be addressed using a global indicator of the compatibility between two similarity measures. Such an indicator can also be used to assess the quality of the situation space similarity measure with respect to the case-based prediction task. We discuss several perspectives opened by such an interpretation of the task of analogical transfer as the optimisation of the compatibility criterion: we explore interactions with similarity learning, as well as with energy function optimisation.