There have been several recent works that integrate LBDD with molecular docking to improve predictive performance. Broccatelli and Brown and Kumar and Zhang utilize LBDD techniques to determine protein conformations that will increase docking performance with a given drug molecule. Liu et al. use protein-ligand interaction fingerprints to increase the scoring power of standard molecular docking scoring functions. Paggi et al. integrated structure-based docking from Glide, a commercial docking software, and 3D ligand similarities to increase pose prediction performance with their docking pipeline ComBind. Their work allows the use of abundant information on known active molecules that have no known bound structure to improve pose selection. Using Glide to dock a diverse protein-ligand benchmark set, they derive two distributions describing the pairwise pose similarity of poses near the ground truth and the pairwise pose similarity of all docked poses with respect to features such as hydrogen bonds or hydrophobic contacts. During the pose selection procedure, the feature distributions are converted to energy-like terms using the log ratio of the near-native similarity distribution to the all-pose similarity distribution. These likelihood terms are used in combination with Glide's pose score to select a pose for a query ligand as well as for a set of ligands known to bind to the same receptor that do not require resolved structures. Their pipeline showed increased pose selection performance over only using Glide's pose score, however, their pipeline utilized closedsource, licensed tools for pose generation and featurization, limiting its adoption by the molecular modeling community.