Regarding the main results, the dataset quality of CPI2M is firstly investigated by overlapping analysis and chemical space visualization to ensure the validity of CPI2M for overall and AC performance evaluation. Then, we conduct extensive comparison experiments on CPI2M and MoleculeACE 23 with internal/external/transfer learning validation dataset setting, highlighting the superiority of GGAP-CPI compared to various baselines on multiple general and challenging scenarios. Regarding activity cliff samples, our results demonstrate the fusion of protein information in GGAP-CPI that transfers target-specific bioactivity prediction task to CPI prediction benefits to more accurate predictions, and integrated ensemble learning on multi-typed bioactivity dataset can further enhance the improvements. Meanwhile, considering practical virtual screening assessment, CASF-2016, Merck FEP, DUD-E, DEKOIS-v2, and LIT-PCBA 36 benchmarks are also introduced, indicating GGAP-CPI is comparable to other widely used scoring function methods with superior ranking and scoring powers. In all, building on previous studies, our study promotes the current investigation of activity cliffs from target-specific exploration to integrated CPI prediction with substantially larger datasets, more comprehensive evaluations, and more effective methodology designs.