We have thus introduced the CryoXKit tool within the AutoDock Suite as a computationally inexpensive and userfriendly method for incorporating experimental structural knowledge into ligand docking with AutoDock-GPU. In doing so, we have shown that this structural knowledge may enhance docking pose prediction with minimal user input beyond supplying the experimental densities themselves, and improves screening active/inactive ranking for select systems. Interestingly, the inclusion of this information does not result in any performance detriment, making this method suitable for prospective screenings with little to no downsides. Given the negligble computational overhead that this method adds with respect to standard AutoDock-GPU dockings, we believe this is a simple and safe method of general applicability for improving virtual screening success rates.