Tab. 2. D-MPNN models performance comparison for EXT-Zamora and EXT-SAMPL9 The single-task D-MPNN model is trained on HF-Exp only and thus serves as a baseline to evaluate whether the inclusion of LF-QC data in the different multi-fidelity approaches can improve prediction accuracy. The single-task model achieves an RMSE of 0.63 log P units and R 2 of 0.86 on the EXT-Zamora and an RMSE of 1.32 log P units and R 2 of 0.65 on the EXT-SAMPL9 dataset. The lower accuracy observed on EXT-SAMPL9 dataset is expected, as this dataset tests the generalization to larger molecules. For completeness, we train also a single-task D-MPNN model on the LF-QC and the models shows comparable performance, with slight differences in RMSE and R 2 values (Table ). Now considering the multi-fidelity approaches, we find that transfer learning, where the model is sequentially trained on the LF-QC dataset and HF-Exp dataset, shows an improvement over single-task training with an RMSE of 0.51 log P units and R 2 of 0.91 on the EXT-Zamora and an RMSE of 1.14 log P units and R 2 of 0.74 on the EXT-SAMPL9 dataset. The multi-target learning approach, which simultaneously trains on both LF-QC and HF-Exp datasets, performs even better, achieving an RMSE of 0.44 log P units and R 2 of 0.93 on the EXT-Zamora and an RMSE of 1.02 log P units and R 2 of 0.79 on the EXT-SAMPL9 dataset. The feature-augmented learning approach, which sequentially trains on LF-QC and HF-Exp datasets, does not perform as well as the multi-target learning approach, with an RMSE of 0.81 log P units and R 2 of 0.78 on the EXT-Zamora and an RMSE of 1.16 log P units and R 2 of 0.73 on the EXT-SAMPL9 dataset. It thus does not improve the predictive quality compared to the single-task model on the EXT-Zamora, but only on the EXT-SAMPL9 dataset. For the overall predictive quality in terms of RMSE and R 2 , multi-target learning thus yields the highest improvement over single-task learning and is therefore most effective, see Table . For the EXT-Zamora dataset, the multi-target learning approach consistently shows the best performance across all molar masses. Only one molecule of 400 g/mol to 500 g/mol is out of the range of ± 1 log P units (see Figure ). The transfer learning approach also performs well, though slightly less effectively for larger molecules >300 g/mol. The feature-augmented learning approach, however, shows higher variability, particularly for the middle-weight range (100 g/mol to 200 g/mol and 200 g/mol to 300 g/mol).