The multi-target learning approach demonstrates the most consistent and lowest absolute differences in log P predictions across various chemical classes. For example, in the classes of alcohols, ethers, and alkyl halides, it shows significantly lower errors compared to feature-augmented learning and transfer learning approaches. Interestingly, multi-target learning shows a great agreement between predictions and experiments with a mean absolute lower lower than 0.5 log P units for the chemical classes aniline, ketone, and aminophenol for the EXT-SAMPL9 dataset and keeps the same consistency for the EXT-Zamora dataset except for the chemical class aminophenol. This indicates multi-target learning effectively captures the distinct characteristics of different chemical structures by leveraging both LF-QC and HF-Exp datasets during training.