In this study, we screened thousands of potential ligands from the Zinc database and utilized AutoGrow4, an open-source structure-based drug design tool, to evolve potent inhibitors into novel drug-like compounds with optimized properties . We present over a dozen lead compounds derived from ten generations of ligand evolution with the highest binding affinities, as well as an evaluation of an entirely open-source route to novel inhibitor identification through the examination of the software's efficacy across generations.