For our validation, we chose a subset of 8 targets from the LIT-PCBA and added two additional systems. First we added HSP90a because we know it is particularly challenging for our scoring function. Then, since all of the LIT-PCBA tar-get structures were determined by x-ray crystallography, we added an additional system (TRPV1) to include one that was determined with cryo-EM. The detailed list of targets and PDBs used for this validation is reported in Supplementary Table . Since the ligands for each target in the LIT-PCBA are experimentally validated active/inactive compounds from highthroughput screening assays, there is a wide range of active/inactive ratios across targets, generally ranging from a few hundred to nearly 20,000 inactives per active. Truchon and Bayly 17 previously determined ratios greater than approximately 250 inactives per active suffer from pseudoexponentially increasing error when quantifying screening performance. For the targets for which there were too many inactive compounds, we reduced the number of inactive compounds to give a standardized ratio of 1:48 actives to inactives. Inactives were selected so that 1/3 of them would be those most chemically similar to the actives, 1/3 to be most similar to the crystallographic ligands, and the remaining 1/3 to be as diverse as possible of the remaining inactives. The selection process for these inactive compounds is detailed in the Supplemental Methods. In this way, we sought to create a maximally-challenging dataset to test the discrimination power of the density guidance method. Two exceptions were made for targets which did not match these criteria. For ALDH1, the ratio of actives:inactives was greater than 1:48 (≈1:19); therefore, all actives and inactives were used. For TRPV1, a 1:52 antagonist:decoy dataset from Llanos et al. was used due to lack of availability of a sufficiently large high-throughput antagonist screening. The numbers of active and inactive/decoy ligands, and sources for each target in our final dataset are also summarized in Supplementary Table . To evaluate the screening performance, each ligand was docked in multiple tautomeric/protomeric forms (hereafter called ligand variants) into all structures available for its target with either a density-modified score or the standard AutoDock4 force field , selecting the best scoring pose for analysis. The protomers for all targets except FEN1 were prepared at pH 7.4. The FEN1 ligands were prepared at pH 12 to ensure that magnesium-binding oxyanions were properly generated (see Methods for further comments). Ligands docked with a density-modified score were then rescored with the standard AutoDock4 scoring function. The best score for each ligand from among all of the receptor structures and ligand variants was used in ligand rankings. The improvements in screening discriminatory power with the use of matched receptor/density guidance is shown in Figure . This improvement was quantified as the average difference between BEDROC score (Fig. ) or Receiver Operating Characteristic-Area Under Curve (ROC-AUC; Fig. ) using the matched density guidance versus with no guidance (i.e. standard scoring function) across 3 replicate guided screenings. BEDROC prioritizes the early enrichment of active compounds, providing a metric more applicable to most real-world drug discovery applications. ROC-AUC, meanwhile, equally weights the discrimination performance over the entire ranked ligand list. For both metrics, the overall trend in our data remains the same. Most targets show comparable screening performance with or without the use of the matched receptor/density guidance, especially for the screening-relevant BEDROC metric. This result confirms that the new method does not impact performance in a negative way, such as by generating an excess of false positives compared to the standard docking method. MAPK1, TRPV1, ADRB2, and MTORC1 show appreciable improvements in BEDROC score with the use of density guidance. Closer examination of the individual replicate ∆BEDROC values and their associated bootstrapped 95% confidence intervals (Sup. Fig. ) reveals all of the replicates for MTORC1 to be significantly different from the null hypothesis of no improvement. MAPK1 and TRPV1 each have 2 replicates which are significantly improved. None of the replicates for ADRB2 are significantly different from the no guidance condition owing to the very small number of compounds (17 actives and 816 selected inactives). For ROC-AUC, TRPV1, ADRB2, and MTORC1 are again improved, with TRPV1 and MTORC1 significantly so (Sup. Fig. ). PKM2 and VDR perform slightly worse with the ROC-AUC metric when using density guidance. Though this difference is statistically significant per the replicates' bootstrapped confidence intervals, it is not likely to impact a prospective virtual screen as indicated by the lack of loss in BEDROC values. We did observe one positive outlier among our replicates in one of the screenings of MTORC1 (Sup. Fig. and). This target exhibited high variance throughout our validation process for reasons that remain unclear. However, exclusion of this replicate does not impact the overall conclusion of MTORC1 performing significantly better per BEDROC and ROC-AUC with the density guidance than without. Finally, we also tested the 1:1 active:known inactive ligand set for TRPV1 published by Llanos et al. , with the results shown in Supplemental Figures and for BEDROC and ROC-AUC, respectively. Using this ligand set, performance with density guidance does not differ significantly from without guidance. However, both conditions perform extremely well in absolute terms (ROC-AUC 0.80), implying that there many not be much room for improvement.