In the talk, I will present the approach to the scheduling of solvers in the domain of Satisfiability Modulo Theories (SMT). Concretely we studied how to apply Graph Neural Networks to predict the performance of SMT solvers on a given problem instance and create schedules based on those predictions. In contrast to related methods, GNNs do not require manual feature design as they enable discovering relevant features in the raw data.