A fable is a short narrative whose purpose is to convey a moral lesson. While semantically related, morals and fables differ fundamentally in form: morals are abstract and propositional, while fables express the same idea through characters and plot. In this work, we ask how well this conceptual relationship is captured in the latent representations of embedding models. To study this, we introduce MoraLink, a benchmark for moral-to-fable retrieval in which models must retrieve the correct fable for a given moral. We find that off-the-shelf embedding models struggle with this task, and that augmenting fable documents with LLM-generated moral-style summaries substantially improves retrieval, identifying document representation as the primary bottleneck. Contrastive fine-tuning further improves performance, and we release MoraLink as a challenging testbed for probing how well embedding models capture the abstraction gap between a moral principle and the narrative that embodies it.