To fully address the challenges associated with creating sustainable, AI-guided workflows, active learning techniques must be integrated into them. While the selection funnels can find a list of hundreds of possible candidate materials, it cannot identify which predictions are the most important to validate next. However, introducing an acquisition function the workflows can then maximize the quality of information gained per calculation or experiment. In turn this will allow us to speed up the discovery of good materials for vital applications. More importantly, by redoing the feature importance study after each iteration we can further refine the screening criteria and continue calculations that were initially discarded because they broke one of the old metrics.