Among the various molecular simulation methods, docking has long been the method of choice for massive virtual screenings evaluating ligands numbering in the hundreds of thousands to millions. However, well known errors and approximations in docking scoring functions lead to inaccuracies in both ligand scores and predicted poses. Several groups, including us , have sought to incorporate new types of data into the docking process to mitigate such limitations, including using known pharmacophoric atoms, density from cosolvent simulations , or experimental data such atomic densities from cryogenic electron microscopy (cryo-EM) or x-ray crystallography . Most of these latter approaches are focused on improved modeling of known ligands into their experimental structural density, as opposed to prospective dockings. GemSpot/GlideEM 3 was one of the first such methods to be introduced, in which dockings are combined with density cross-correlation (in a process taking from minutes to days per ligand ) and quantum mechanical refinement. RosettaEMERALD 4 , ChemEM , and GOLEM have all been introduced more recently with different methods for incorporating the provided cryo-EM density into the pose prediction algorithm. These methods all have comparable runtime to GemSpot, and no applications reported using x-ray crystallography data. Here, we introduce CryoXKit, a new toolkit in the AutoDock Suite, which allows the direct incorporation of experimental x-ray crystallography (XRC) electron density and/or single-particle cryo-EM Coulombic potential maps (hereafter both referred to interchangeably as density maps) into the affinity maps used to perform the dockings, introducing no runtime overhead to the docking itself other than the time required to modify maps themselves. We report improved performance in re-dockings and crossdockings using both crystallography and cryo-EM structures, with improved RMSD accuracy with respect to experimental poses. We also validate that the inclusion of experimental ligand density in the docking scoring function improves prospective discrimination of virtual screenings for some targets without adding significant performance degradation to others. This method requires little to no prior knowledge about the target or specific interactions other than experimental density maps, representing an accessible and targetagnostic approach to improve diverse docking applications.