Molecular docking is a widely used for ligand discovery, both in industry and academia . The goal of docking is to predict the binding affinity and pose of small molecules in the binding site of a target protein. The method can screen libraries of billions of molecules and, unlike ligand-based methods, often discovers novel ligands entirely unrelated to those previously known . In some cases, docking can lead to the discovery of compounds in the sub-nM range , with some of these being active in vivo . However, compared to other techniques in computational biology, such as homology modeling and sequence database searching , docking as a procedure remains labor-intensive and intimidating to new users, thereby limiting its wider adoption and hindering its application on a proteomic scale. Docking software is typically complicated and comes with a steep learning curve, making it difficult to use to its full potential. This is especially true during the model optimization stage of the docking process, which involves fine-tuning numerous parameters of the model to improve its accuracy and reliability. It does not help that, even when performed by experts, docking can still sometimes fail to accurately reproduce experimentally determined binding characteristics for some targets. These liabilities have diminished the technique's overall impact, not only by deterring researchers coming from limited computational backgrounds, but also by making it arduous even for experienced computational researchers to deploy docking models at a large scale on the order of billions of molecules.