One method that has seen potential towards the utilization of smaller datasets in deep ML is transfer learning. In this process, a model is first trained on a large dataset. The target prediction of the first task (pretraining task) does not need to be directly related to the desired final task (finetuning task), however, the initial knowledge gained from pretraining must have some relevancy to the finetuning. In a neural network, each non-linear function is referred to as a layer; each layer in the neural network is responsible for extracting relevant chemical features for a given task. The first layer is defined as the input layer, the final layer as the output layer, and for this manuscript, the penultimate layer is defined the latent space (Figure ). One may imagine the latent space as a complete digitization of a molecule, whereby each molecular feature has been assigned a series of numbers. The key to effective transfer learning hinges around this latent space, whereby each molecule's chemically relevant features are so well characterized that the substitution of one output layer for another output layer results in accurate prediction of a different chemical property. In essence, the model transfers the knowledge it learnt from pretraining to finetuning. To date, transfer learning in data-driven chemistry has been applied on a case-by-case basis, where pretraining tasks are expertly chosen for specific finetunings to minimize domain mismatch. This limits the possibilities of fulling harnessing smaller datasets.