We utilize three multi-fidelity approaches to enhance the prediction of molecular properties: transfer learning, feature-augmented learning, and multi-target learning (see Figure ). Transfer learning (cf. Refs. )) leverages pretrained models on LF-QC dataset to fine-tune predictions on the HF-Exp dataset. The idea is to use the low fidelity QC data (LF-QC) to develop a broadly applicable model and then employ the high fidelity experimental data (HF-Exp) to increase model's accuracy, thus enhancing the model's predictive capability with limited high fidelity data. Feature-augmented learning (cf. Ref. ) combines the HF-Exp dataset and LF-QC dataset: first a model is trained on the LF-QC dataset and then the predictions are used as an additional feature to existing ones for training a new model on the HF-Exp dataset. The purpose of feature-augmented learning is to integrate data of varying fidelities with high correlation to improve the predictive accuracy. Multi-target learning or multi-task learning (cf. Refs. ) simultaneously predicts both experimental (HF-Exp dataset) and synthetic (LF-QC dataset) properties using a single model, aiming to exploit the interdependencies between different properties. This approach therefore aims to utilize information from multiple related tasks (predicted and experimental data) to improve the overall learning process and model robustness.