When performing experiments, it is advantageous when these experiments are widely applicable and serve multiple purposes. The information gathered in experiments should be made available according to the FAIR-principles and can then be of value for other researchers . However, with active machine learning one objective is chosen which determines the experimental selection. This hampers the applicability of the experiments as only one experimental output is well-studied. For example when investigating reactions, the conversion is typically selected as output of interest but this limits the information on other properties such as yields or selectivities. In the worst case, the yields are not measured and no information is gathered, on the other hand when these yields would be measured no guarantee is given that all trends are considered in this example. As the goal of the active machine learning was to model conversions, it ignores the behavior of interesting reaction yields which can result in trends to remain hidden. With Bayesian optimization this does not pose an issue as the goal here is to optimize an objective, which makes the data per definition less generally applicable. Multiobjective Bayesian optimization techniques exist while for active learning only single objective strategies are possible, meaning that all interesting outputs should be incorporated within the single active learning objective . Therefore, to ensure the reusability of the gathered data, it is important that during experiments not only the modeled output is measured but also the potential other relevant outputs.