Another widely utilized benchmark is the Directory of Useful Decoys Enhanced (DUD-E), which was specifically designed to address the reporting bias where only binding affinities for known binders are typically disclosed, leading to a scarcity of data on non-binders. The DUD-E dataset includes "decoys" for each active ligand at a ratio of 50 to 1, containing over 1.4 million compounds, making it one of the largest database for benchmarking scoring functions . This dataset however, has drawn scrutiny due to its method of selecting decoys that are chemically similar yet topologically dissimilar to the active compounds. Such bias resulted in models that excel in benchmark tests by exploiting these chemical versus topological discrepancies rather than learning the fundamental principles of protein-ligand interactions. It also led to models with poor generalizability when applied outside the specific context of the dataset . While the use of decoys can be useful, care must be taken in avoiding bias when curating decoy datasets .