Short Linear Motifs (SLiMs) are short, disordered peptide fragments, which mediate a large class of protein-protein interactions (PPIs). SLiM-mediated interactions are often dynamic, low affinity interactions, which play a crucial role in cell regulation and signal transduction. Despite their importance to cell function, complete characterization of SLiMs, both in terms of binding partners and diversity, as well as consolidation into a unified dataset, is bottlenecked by experimental throughput as well as the difficulty of extracting and aggregating motif information across numerous papers and experiments. Currently, only a minuscule fraction of the estimated hundreds of thousands of SLiMs have been identified . Furthermore, the limited number of experimentally validated SLiM-protein interactions has made de novo SLiM discovery via computational methods challenging . Up until now, computational methods for de novo SLiM discovery task has been too challenging with most progress centered around the non-de novo setting which leverages extant evolutionary data. However, recent progress in protein structure prediction has translated to significant progress across many applications, so we posit that protein structure prediction networks may make de novoSLiM discovery tractable. In this work, we curate a SLiM discovery benchmark dataset, devise an AlphaFold-Multimer-based SLiM discovery method, and demonstrate settings in which our method can accurately perform de novo SLiM discovery.