The feature-augmented learning approach shows the highest absolute differences in several chemical classes, including ketones and benzene derivatives. This performance suggests that the method's sequen- tial training on LF-QC and HF-Exp datasets may not be as effective in capturing the detailed chemical properties required for accurate log P predictions. The higher errors in the ketone class, particularly in the SAMPL9 dataset, highlight the approach's difficulty in balancing data contributions from different fidelities, especially for complex chemical structures. This indicates that feature-augmented learning requires careful handling to avoid poor performance in chemically diverse datasets, especially when few data is available for fine-tuning.