Through our work, we have demonstrated that incorporation of ligand-derived experimental structural density improves docking pose accuracy and, for select protein targets, virtual screening performance with AutoDock-GPU. We were able to achieve this improvement through inexpensive summation of the weighted experimental density as an additional force field term for the heavy-atom atom maps. Therefore, our method introduces no additional runtime requirements to the dockings themselves, unlike previously reported correlationbased methods which can take minutes to days to predict the pose of a single ligand. Our method also does not require particularly high-resolution density as input, as shown by good pose prediction success rates with degraded resolution maps cryoXKit up to 4 Å. Finally, we have allowed for density data from multiple sources to be combined to enhance pose prediction, especially for cross-dockings where the holo structure match to the ligand may be inexact.