Another example of constrained experimental units are high-throughput experimental campaigns which are for example used to screen different catalytic materials. Within these units, several experimental variables such as temperature and pressure are often fixed for every type of experiment per batch. This requires another constraint on the batch selection of these experiments as variables need to be fixed for all selected queries. To tune the active machine learning algorithms according to their application, a close collaboration between the machine learning expert and experimentalist is thus required. In this way, the benefits of applying active machine learning are also available to less flexible experimental units. This symbiosis between experimentalist and machine learning scientist will benefit both parties.