Taken together, our analysis suggests that ML models' success in this case was primarily driven by "translating" DEL hits into catalog compounds, rather than learning specific structural rules governing activity. While this approach appears edective for identifying viable hits, it raises concerns about the model's generalizability beyond the molecular structures present in the DEL used for training, a topic we explore further below.