Multi-fidelity learning also has applications in active learning and Bayesian optimisation, where one aims to iteratively optimise a black-box function using a fixed budget of high-fidelity evaluations. The main motivation for this setup is the desire to leverage low-fidelity simulations to eliminate regions with low function values using an inexpensive proxy for a targeted property of interest. The expensive highfidelity evaluations are then used in small but promising regions to quickly find the optima. Fare et al. have used a multi-task Gaussian process in this setting for materials design and screening of molecules. While Gaussian processes have the advantage of being able to perform uncertainty quantification, the computational complexity (cubic) can be a challenge for large scale low-fidelity datasets. The ically in a typical drug discovery scenario with a large and low-fidelity (LF) high-throughput screening dataset, and a sparse and high-fidelity (HF) confirmatory screening dataset. Graph convolutions are used to propagate and learn atom-wise representations according to the connectivity imposed by the bonds, which are then aggregated into a single molecule-level representation or embedding (a fixed-dimension vector). The readouts are standard pooling functions, e.g. sum, mean, max, or neural networks (adaptive aggregators). The symbol ∥ denotes concatenation, µ(x) and σ(x) denote the mean and standard deviation learnt by the VGAE, and 'Dense NN' is a multi-layer perceptron. The four workflows presented in this figure are listed in the top right and correspond to the first four experiments presented in Results. A low-fidelity model is first trained with supervised information to produce latent space embeddings z * (E1). A separate model with the same architecture can then be trained to predict high-fidelity values, by concatenation with either the actual labels (E2) or embeddings/predictions generated by the LF model (E3). A novel strategy unique to adaptive readouts in graph neural networks is that of pre-training a model on LF data as in (E1) and then fine-tuning exclusively the adaptive readouts with the VGAE layers being frozen (E4). We also emphasise that the learnt low-fidelity embeddings/predictions can be integrated into any machine learning algorithm (e.g. support vector machines, random forests, etc.).