To address scarcity of molecular property data, previous literature studies have employed various multi-fidelity learning approaches. A recent review by summarizes the different multi-fidelity methods, suggesting that pretraining models on low fidelity data such as a large dataset derived from Quantum Chemical (QC) calculations and semi-empirical models, followed by fine-tuning with high fidelity data such as experimental data, can significantly enhance their applicability and reliability in predicting molecular properties. In particular, three multi-fidelity approaches are promising in molecular ML: transfer learning, feature-augmented learning, and multi-target learning . Transfer learning leverages pretrained models to improve predictions, feature-augmented learning integrates predictions as additional features, and multi-target learning simultaneously predicts multiple related properties. To this end, to overcome the challenges posed by limited experimental data in predicting toluene/water log P of neutral species, we investigate these three multi-fidelity learning approaches that leverage QC and experimental data to increase the effectiveness of GNN models.