The development of active machine learning algorithms is often done on simulated data where there are no practical limitations on the data creation side . However, in real-life experimental units or procedures do not allow this flexibility. For example even a completely automated experimental unit often needs to heat up or cool down or time to stabilize, which slows down the generation of a new datapoint when different temperatures are selected by the algorithm. Additionally, experiments are often performed in parallel (e.g. in high-throughput units) as opposed to the algorithms which assume a sequential selection of experiments. Therefore, active machine learning strategies should be constrained to the unit on which they are used, to allow for an optimal experimental efficiency to make them applicable to "Real World applications" . In the example above it is often easier to heat up an experimental unit than to cool it, therefore an extra constraint should be added to the algorithm which prefers to select experiments which increase in temperature rather than decrease in temperature.