Hit identification is an essential and challenging step in the drug discovery process due to limited understanding of disease biology or target complexity, as well as the constraints of screening assays . High-throughput screening for hit compound identification, can be both costly and time-consuming , which limits the number of the potential targets that can be screened and the diversity of hit chemicals for each target. To address these challenges and expedite the drug development process, innovative computational tools are being extensively employed . While traditional structure-based virtual screening plays a vital role in identifying diverse hits through discriminative models, there is still considerable room for improvement in terms of hit rate and chemotype diversity in the early hit discovery stage .