We apply various ML models and multi-fidelity learning approaches to predict the toluene/water log P of neutral species. Initially, we generate a low fidelity QC dataset consisting of approximately 9,000 toluene/water log P values of neutral species using the COSMO-RS approach, which we chose due to its balance of accuracy and computational efficiency. We use this dataset to pretrain GNN models, so they encompass a wide range of chemical classes and atom types. We then fine-tune and test the pretrained GNN models with different multi-fidelity learning approaches using the high fidelity datasets of and the SAMPL9 blind challenge. Specifically, a part of the Zamora dataset, comprising 250 experimental log P values, is used for fine-tuning while the remaining part is reserved for testing, similar to the approach taken with the SAMPL9 dataset, which includes 16 experimental log P values. Next, we compare the GNN models with a GNN trained only on the experimental data and additional semiempirical and data-driven approaches for the prediction of toluene/water log P . Finally, we discuss the strengths and limitations of the different approaches. Thereby, we address how multi-fidelity strategies leveraging both QC and experimental values can play a crucial role in ML for accurately predicting molecular properties, especially when only a limited amount of experimental data is available. is shown in Table . The SMILES from all molecules used in this study are provided in the supporting information as a CSV file.