Machine learning and deep learning-based scoring functions are highly sought after for virtual screening purposes due to their relative ease of training and predictive capabilities. These methods offer a significant advantage over traditional scoring functions, which typically require significant time and extensive computational resources. Advances in structure prediction models such as AlphaFold2 45 and ESM2 , coupled with ML-based SFs like RFscore 9 and DL-based SFs such as GNINA or AtomNet , have alleviated the challenges of crystal structure absence and the time-consuming nature of docking large libraries. Additionally, the possibility of designing novel ligands or proteinligand poses de novo through generative models suggests the feasibility of a fully ML/DL-driven virtual screening pipeline.