Previous studies have shown that many well-performing DL-SFs experience significant performance drops when using Alphafold 2-predicted structures, indicating their limited generalizability and overconfident performance results in unknown binding pose scenario. Moreover, since they are generally trained on PDBbind datasets with similar data distributions as CASF-2016, to achieve a fairer comparison between GGAP-CPI-IntEns and other SFs, we finetuned GGAP-CPI-IntEns on PDBbind-v2020 general set data with Alphafold 2-predicted protein structures and tested it on CASF-2016. The results, labelled as "GGAP-CPI-IntEns-ft", indicated that with crystal structure information available for model finetuning, the performances of GGAP-CPI-IntEns were much improved (0.857 vs. 0.679 for scoring power; 0.818 vs. 0.649 for ranking power). These results outperform the most competitive ∆-learning-SFs and DL-SFs which were also trained on PDBbind data. The results for GlideScore-XP and CarsiDock are collected from Ref. The results for BIND are reproduced based on the released model. This assessment on CASF-2016 and MerckFEP demonstrates the ability of GGAP-CPI for practical virtual screening, especially for power of accurately predicting the specific affinities and ranking binders with the highest efficacies. This would highlight GGAP-CPI's potential in the hit optimization task, which focuses on differentiating strongest binders among a set of structurally similar ligands.