Future work will aim to refine the CPI2M benchmark with pairwise AC annotations to enhance the granularity of AC effect estimation. Moreover, to improve the utility of GGAP-CPI in practical virtual screening settings, which often suffer from a high sensitivity to false positives, we plan to incorporate more experimentally verified negative measurements and develop binder/non-binder classification benchmarks. These enhancements will involve optimizing the GGAP-CPI architecture using techniques such as Siamese networks and supervised contrastive learning to better capture core substructures for pairwise AC molecule inputs. With these ongoing developments, the CPI2M benchmark and the GGAP-CPI model are anticipated to become increasingly suitable for practical, structure-free virtual screening.