Our final feature augmentation amounts to learning a latent representation of molecules in the lowfidelity domain using a supervised autoencoder (Section 4.4), then jointly training alongside the latent representation of a model that is being fitted to the high-fidelity data. This approach also lends itself to the inductive setting. More formally, transfer learning in this case can be achieved via the feature mapping Ψ Embeddings (x) = (ψ S (x), ψ T (x)) where ψ S (x) is the latent embedding obtained by training a supervised autoencoder on low-fidelity data D S , and ψ T (x) represents the latent representation of a model trained on the sparse high-fidelity task. Note that ψ S (x) is fixed (the output of the low-fidelity model which is trained separately), while ψ T (x) is the current embedding of the high-fidelity model that is being learnt alongside ψ S (x) and can be updated.