From data fitness aspect, one critical assay assessment phenomenon -activity cliff (AC) -could significantly influence the affinity prediction performances. AC is defined as pairs of structurally similar compounds but having significant differences in bioactivities, which represents the discontinuity in activity landscapes of affinity/bioactivity datasets and in structure-activity relationships. For ML-based data-driven approaches that are always trained based on "chemical similarity principle", the AC molecules are typically recognized as abnormal samples and lead to the inaccurate affinity/bioactivity prediction and the failure of differentiating ACs and non-ACs. Meanwhile, the small structure changes between positive ACs and negative ACs will cause high false positive successful rate that is particularly harmful for virtual screening methods21. Previous AC studies have explored and emphasized the large performance differences on ACs and non-ACs for target-specific bioactivity prediction models, while some studies applied AC concept for binding affinity and bioactivity prediction-related tasks such as hit-to-lead optimization and structural alert development. Although the AC concept has been well-defined, the AC-induced performance discrepancies in CPI binding affinity and bioactivity prediction domain remains uncertain. Firstly, the identification of ACs is thought to relate to the target structures and binding conformations. Therefore, compared to most previous studies for target-specific bioactivity prediction models that only takes ligand features into account, CPI methods generate predictions based on both ligand and protein representations thus may potentially alleviate AC discrepancies. Besides, since ACs are not "isolated" pairs that over 90% of ACs are formed by groups of structural analogs with varying potency, larger-scale involved data that better depicts the AC landscapes may also benefit CPI prediction performance on AC molecules. Meanwhile, experimental uncertainty and CPI studies suggested the high correlations between different bioactivity measurements and the performance gain from joint training, which may contribute to better overall and AC performances. However, the limited targets in current AC benchmarks hinder valid and comprehensive AC estimation for CPI methods from data availability aspect.