Active learning is considered a good starting point for autonomous experimental science since it works pretty well with small datasets. Data acquisition is the most significant challenge in learning projects that involve real-world experiments. Unlike in the virtual world, each datapoint in the real physical world could be fairly expensive and timeconsuming to obtain -often a dataset of 1000 points is considered substantial. Given these conditions, the strategy for sampling the design space becomes of paramount importance. The primary function of active learning is to interactively suggest the parameter combination to test in the next batch, backed by rigorous mathematical principles. There are various nice frameworks on GitHub, and the one we implemented in CRESt is the Ax platform, developed by a team at Meta, and built upon BoTorch. Ax offers a well-implemented SQL storage option, allowing one to resume the previous active learning campaign even if the GPT backend is reset, by retrieving the history stored in the database.