To move from this no man's land and leverage the power of ML for DFAs design, we need to solve the underlying transferability problem. Helping strategies are the use of physical constraints (a mix of old-and new-school methods), more diverse data in the training set , or the engineering of new DFT features . Yet, all these approaches neglect a crucial fourth angle-training data efficiency. This aspect is particularly important given that feeding more data to ML models can become a never-ending game due to the data hunger in ML models and the vastness of chemical space . The purpose of this work is to critically examine data efficiency in training DFAs and propose a strategy to maximize it in order to embed transferability in ML of DFAs.