AI-guided workflows have the potential to revolutionise materials discovery frameworks by focusing calculations or experiments on the most promising materials, and potentially remove the initial bias of data selection. By using an appropriate acquisition function to determine which experiments or computations to run next, we can automate these calculations. In turn the focus of the researchers working on these problems can instead be on further developing new methods and not managing a large set of calculations. Furthermore, explainable-AI methods will help elucidate why the models are deciding which candidates to calculate next. With this insight, part of the physical mechanisms driving, facilitating, or hindering the different processes may also be understood. Finally, as the frameworks become better focused the overall efficiency of these efforts will be significantly enhanced.