and RTMScore. In addition, the DEKOIS-v2 benchmark, which features 81 targets, 3,239 active compounds, and approximately 97,000 decoys, further demonstrated the strength of our approach: GGAP-CPI-IntEns achieved an EF 1% of 28.73, marking a 17.46% relative improvement over the next best baseline (Table ). For LIT-PCBA, an unbiased bioactivity classification dataset containing 15 targets with 8,020 actives and 2,675,399 inactive molecules (a positive-to-negative ratio close to 1:1000), our method displayed competitive performance relative to other CPI-based scoring functions (Table ). Moreover, when compared with widely used scoring functions that incorporate docking procedures (Table ), GGAP-CPI-IntEns not only achieved comparable screening power but also offers the distinct advantage of being completely independent of preprocessed docking or crystal structures, thereby delivering enhanced speed and flexibility for large-scale virtual screening.