We mitigate the lack of suitable atomistic models of such heterogeneous surfaces by combining an evolutionary search algorithm with a transferable MLFF to identify valid candidate structures. Transferability, in particular, is an important prerequisite for minimizing the computational cost associated with generating training data. We demonstrate that by utilizing small, well-known reconstructions and implementing a careful data selection routine built on structural and spatially-resolved local uncertainties, we can arrive at an MLFF capable of extrapolating to larger, more complex structures. In the following, we first discuss the active learning approach. We then proceed to show how structural models reproducing the STM images can be systematically obtained for all the coexisting surface structures.