Research has hence focused on estimates of uncertainty as proxies for the error incurred by using a given MLFF. When that error is suspected to exceed tolerable margins, retraining with an expanded training set can help extend the applicability of the MLFF. In such scenarios, an efficient algorithm must aim toward issuing only as many ab-initio calculations as required, while preventing waste of resources on redundant or irrelevant configurations. Thus, the need for an uncertainty metric to evaluate the quality of the results dovetails with the usefulness of such metrics for identifying or even generating optimally informative new configurations. This makes stochastic structure searches naturally part of the domain of application of active learning (AL) workflows, where an informed data selection is achieved through uncertainty estimation. This work focuses on the less well-understood Ti-rich surface reconstructions at the (110) orientation of SrTiO 3 . Literature describes numerous composition-related surface reconstructions that can be broadly grouped into two families, characterized by Ti-poor (n × 1) and Ti-rich (2 × m) overlayers on an otherwise unchanged bulk. Here, n and m denote the number of (1 × 1) bulk unit cells covered in the [001] and [ 110] directions, respectively. Figure shows examples of both variations. Ti-rich overlayers pose the particular challenge that, even when the Ti-to-Sr ratio is controlled, numerous reconstructions lacking corresponding DFT models can coexist. Here, we again observe pronounced heterogeneity in new STM measurements and are able to identify varied reconstructions (see Fig. of the SI).