Here we present an easy-to-use, open-source alternative to ComBind that we call Open-ComBind. The open-source, deep learning-based molecular docking software gnina is used to generate high quality ligand poses, and open-source tools like ProDy and RDKit are used to featurize the docked compounds. Using a likelihood framework identical to ComBind, we utilize a set of helper ligands without known structural information to improve the pose quality of our docked ligand. The pose selection procedure strikes a balance between the individual pose scores assigned by the per-ligand deep learning scoring functions and the similarities and differences between the poses of various ligands. As a result, Open-ComBind produces a set of optimized poses for all ligands simultaneously, without requiring a shared ligand scaffold or knowledge of any ligand's binding pose.