The proposed framework has been evaluated on a heterogeneous collection of transfer learning tasks, including a novel drug discovery collection of 37 different protein targets (more than 28 million unique experimental protein-ligand interactions in total) and 12 quantum properties from the dataset QMugs (around 650K drug-like molecules). The analysis involves several different baselines, ranging from graph neural networks to random forests and support vector machines. Our empirical results highlight the importance of transfer learning in low-data regimes, which are encountered in drug discovery projects relying on high throughput screening and are not uncommon in quantum simulations. More specifically, we vary the size of the training set on high-fidelity data and show that transfer learning can improve the accuracy of predictive models by up to eight times while using an order of magnitude less high-fidelity training data. In the transductive setting, we notice that the inclusion of the actual low-fidelity label typically amounts to performance improvements between 20% and 60%, and severalfold in the best cases. However, out of the total of 51 transductive experiments, transfer learning via label augmentation was the best performing method in only 10 instances, with the novel graph neural network schemes proving as the most effective 80% of the time (Table ). For the more challenging inductive learning setup, we notice substantial improvements in performance due to latter methods, typically between 20% and 40% in the mean absolute error and up to 100% in R 2 . These performance improvements are mainly due to alleviating the shortcomings of non-adaptive readouts in classical graph neural networks. While on quantum mechanics problems standard graph neural networks are competitive, particularly if they are extensive and non-local (which agrees with the existing literature), they are still outperformed by predictions on sparse high-fidelity tasks. The illustration depicts a typical drug discovery scenario where a large dataset of noisy observations is followed by a sparse, high-fidelity set of measurements obtained via expensive and time-consuming assays. The workflow (steps 1, 2, 3) is general and can be applied to other settings, e.g. quantum mechanics. (Top panel) A traditional HTS (high-throughput screening) experiment generates a massive but noisy set of low-fidelity measurements (primary screening, measuring the activity of molecules at a single concentration, indicated using the format '@ concentration'). An orders-of-magnitude smaller set of molecules is selected for a high-fidelity experiment, such as a confirmatory screen. (Middle panel, bottom) Our proposed transfer learning framework illustrated with three high-level steps in a drug discovery context. Firstly, the corresponding low-fidelity single dose (SD) and high-fidelity dose response (DR) data are assembled into a multi-fidelity dataset. DR values correspond to confirmatory screens measuring a 'pXC50' activity value (the effect X ∈ {I = inhibitory, E = effective, A = activatory}), representing the concentration required for a 50% effect. DR is only available for a fraction of the entire dataset, hence some compounds are not available ('N/A'). Secondly, a graph neural network (GNN) is trained on the large primary screening dataset (low-fidelity or LF), modelling an extensive and diverse chemical space of interest. Finally, at the third step, the molecular structure, supplemented with the molecular fingerprint/representation learnt at the second step, is used to train models of confirmatory activity (high-fidelity or HF), including both deep learning and classical algorithms.