To explore the capability of GGAP-CPI in providing structural insights, specifically in iden- tifying pocket residues, we used GGAP-CPI without fine-tuning on the PDBbind-v2020 general set as the reference model. For each protein-ligand pair in CASF-2016, we extracted the cross-attention matrix and assigned attention coefficients to the corresponding protein residues. To quantify the results, we analyzed the distribution of TopN enriched residues (N=the number of pocket residues for a given protein) for proteins in CASF-2016, as shown in Fig. . The results indicate that GGAP-CPI can effectively retrieve notable proportions of pocket residues compared to the overall distribution. Specifically, the enrichment factor for retrieving pocket residues was found to be 5.506 ± 5.533. Furthermore, there was a significant difference (P = 0.0016, T-test) between the average attention coefficients for pocket residues and other residues (Fig. ). This suggests that GGAP-CPI assigns higher attention to residues involved in binding pockets, demonstrating its potential for practical applications in virtual screening. Two case studies, represented by PDB IDs 3GY4 and 3KWA, are illustrated in Fig.