We explore a new approach to \textit{library learning}, the task of extracting a reusable library of functions by finding common structure in a corpus of programs. We build on two related approaches to this problem: Stitch, which frames library learning as a top-down program synthesis problem, and Babble, which approaches it via an anti-unification procedure over e-graphs. Both of these techniques have shortcomings: Stitch’s top-down search cannot leverage equivalence relations, and Babble’s anti-unification algorithm is inherently quadratic in the size of the corpus. In this work, we introduce E-Stitch, which extends Stitch’s corpus-guided top-down search to operate over a corpus in an \textit{e-graph}. This combines the efficiency of the top-down approach with the expressivity afforded by equivalence relations. We provide a preliminary evaluation of our approach on the benchmarks from Babble, and are able to outperform it in both speed and compression.