Active learning provides one way of combining AI models and high-throughput workflows The goal of this algorithm is to balance exploitation and exploration to select new data points that can optimize a property or better train an AI model for a given material property, while simultaneously finding a global optimum for it in a data-efficient manner. By using an acquisition function that balances exploration and exploitation to select which materials to calculate next, these frameworks can improve the efficiency of the studies by selecting which materials enter the funnel in a non-subjective manner (see the roadmap paper by Boley et al.). In essence, both the active learning framework and the selection funnels attempt to achieve the same goal, that can be achieved synergistically: Active learning suggests which materials enter the workflows and the high-throughput funnel removes the unpromising candidates after each step of the workflow.