This study uses graph neural networks to explore electricity-network switching decisions without solving every candidate as a full alternating-current optimization problem. Separate models predict whether a topology is feasible and estimate its operating cost. Sampling and a tree-based search then identify promising switching plans. The approach seeks faster screening of network configurations under changing demand. This is an SSRN preprint, not a claim of peer-reviewed publication.