First of all, it will extend the fields of application for active machine learning as researchers become more aware of the benefits of active machine learning. This close collaboration will help in identifying useful features within these active machine learning algorithms such as blocking, or automatic post-processing. More practical constraints might be added to the experimental selection, such as the time or cost required for a proposed experiment. Lastly, this collaboration between experimentalist and machine learning expert helps in informing experimental researchers and remove the currently existing biases on active machine learning.