Similarly, for the EXT-SAMPL9 dataset, the multi-target learning approach maintains the best performance across most weight categories (see Figure ). It shows particularly strong results for light molecules and less strong results for heavier molecules. Transfer learning remains competitive but again shows slight performance degradation for heavier molecules. The feature-augmented learning approach continues to exhibit higher variability, especially for molecules in the 200 g/mol to 300 g/mol and >500 g/mol.