Recent advances in structural biology have led to the publication of a wealth of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule structures, including many complexes with small molecules of interest for drug design. While it is common to incorporate information from the atomic coordinates of these complexes into docking (e.g., pharmacophore models or scaffold hopping), there are limited methods to directly leverage the underlying density information. This is desirable because it does not rely on the determination of relevant coordinates, which may require expert intervention, but instead interprets all density as indicative of regions to which a ligand may be bound. To do so, we have developed CryoXKit, a tool to incorporate experimental densities from either cryo-EM or XRC as a biasing potential on heavy atoms during docking. Using this structural density guidance with AutoDock-GPU, we found significant improvements in re-docking and cross-docking, important pose prediction tasks, compared with the unmodified AutoDock4 force field. Failures in cross-docking tasks are additionally reflective of changes in the positioning of pharmacophores in the site, suggesting it is a fundamental limitation of transferring information between complexes. We additionally found, against a set of targets selected from the LIT-PCBA dataset, that rescoring of these improved poses leads to better discriminatory power in virtual screenings for selected targets. Overall, CryoXKit provides a user-friendly method for improving docking performance with experimental data while requiring no a priori pharmacophore definition and at virtually no computational expense. Map-modification code available at: https://github.com/forlilab/CryoXKit.