The MLR and RFR models from show varying performance. Both models achieve higher accuracy on the EXT-SAMPL9 dataset compared to the EXT-Zamora dataset. The high predictive accuracy on the EXT-SAMPL9 indicates the effectiveness of using molecular descriptors when available training data is limited, which has also been reported in recent comparisons of ML/GNN models with and without using QC descriptors . However, these models are typically limited in their generalizability to molecules dissimilar from the training data. The higher accuracy on the presumably more distinct EXT-SAMPL9 set compared to EXT-Zamora (cf. Section "Dataset") is thus unexpected. In fact, we find that the experimental data used for fitting contains a duplicate entry with EXT-SAMPL9, indexed as entries 79 (Aflukin) and 266 (Quinine) . This duplication might explain the better performance observed on the EXT-SAMPL9 dataset compared to the EXT-Zamora dataset. We thus find lower accuracy of the MLR and RFR compared to the ML models for EXT-Zamora and slightly reduced accuracy for EXT-SAMPL9.