Currently, there exists a knowledge gap between the experimentalist community and the machine learning experts . This is at the origin of active machine learning not yet being systematically applied by the former. First, there is a misconception that for active machine learning "big data" is mandatory and an enormous experimental campaign is required to make it feasible. Nugraha et al. found an optimal catalyst composition performing only 47 from a total of 5151 possible experiments . Similarly, Schweidtmann et al. identified their paretofront after 68 experiments for a four dimensional reaction optimization . Moreover, Ureel et al. showed that active learning strategies are already beneficial for experimental campaigns consisting of as little as 18 experiments . These examples illustrate that both active learning and Bayesian optimization are already feasible for smaller datasets.