Molecular docking is a vital tool in the SBDD toolbox that predicts the noncovalent binding of two molecules in three-dimensional space. Knowledge of the ligand binding pose is critical for many downstream drug discovery tasks, such as lead optimization, affinity prediction, and virtual screening. Typical molecular docking algorithms either utilize energetics of interactions or the statistics of known binding interactions to predict the relative placement of the molecules . Conventional docking algorithms, such as Autodock Vina or Glide , and even deep learning-based docking algorithms, like gnina or DiffDock , predict the binding of every ligand separately even when multiple ligands are docked to the same receptor structure. Many drug targets are unsuitable for molecular docking due to a lack of suitable crystal structures (i.e. large ligand volume difference with cognate ligand or no available apo crystal structures). In these cases, alternative approaches must be employed.