As a first step, we provide an evaluation of the multi-fidelity integration performance for all 60 datasets based on the multi-fidelity modelling workflow recently proposed by . In this context, the machine learning task is formulated as a supervised learning problem on confirmatory data, i.e. molecules with DR measurements. Thus, the simplest task is training and evaluating models exclusively on data points with DR labels, corresponding to training sets with typically less than 2,000 instances, and ignoring the orders-of-magnitude larger SD datasets. Multi-fidelity modelling is achieved through two augmentation strategies that integrate information derived from the SD data. The first augmentation corresponds to the inclusion of the experimentally-determined SD activity value in the internal representation (fixed-dimension vector) of each compound in a machine learning model, corresponding to an append/concatenation operation. All compounds that possess DR activity values typically have an associated SD value, making this strategy generally applicable to HTS datasets. The second augmentation requires a separate machine learning model (here, a graph neural network) to be trained on the entirety of the available SD data in a supervised fashion. In this process, the GNN learns to produce SD embeddings, low-dimensional vector representations that carry the molecular information and its SD activity. The resulting SD embeddings can be appended to the internal representation of a machine learning model exactly as for the first augmentation, with the important advantage that the embeddings can be produced for molecules which were not seen during training. Both augmentations are applicable to any machine learning method operating on fixed-dimension vectors.