Our results show variable performance depending on the target (Figure ). The most challenging target is TP53, on which no model could consistently retrieve actives among the top 10 scoring molecules. Notably, this target has the most challenging test set, where inactive molecules are similar to the actives of both the training and the test sets (Figure ), potentially indicating the presence of activity cliffs . MAPK1 and mTORC1 also challenge the CLMs; here, S4 retrieved more active molecules than the benchmarks, especially in the early portions of the test set. PKM2 and GBA are the easiest datasets; here, all CLMs identified bioactive molecules in their top 10, with S4 achieving the highest median across the board. A Wilcoxon signed-ranked test on the pooled scores across datasets supports the superior performance of S4 compared to the benchmarks (p < 0.05), and of GPT compared to LSTM (p < 0.05).