Abstract Designing high-performance algorithms for computationally hard problems is a diffificult and often time-consuming task. In this work, we demonstrate that this task can be automated in the context of stochastic local search (SLS) solvers for the propositional satisfifiability problem (SAT). We fifirst introduce a generalised, highly parameterised solver framework, dubbed SATenstein, that includes components gleaned from or inspired by existing high-performance SLS algorithms for SAT. The parameters of SATenstein control the selection of components used in any specifific instantiation and the behaviour of these components. SATenstein can be confifigured to instantiate a broad range of existing high-performance SLSbased SAT solvers, and also billions of novel algorithms. We used an automated algorithm confifiguration procedure to fifind instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances. Overall, we consistently obtained signifificant improvements over the previously best-performing SLS algorithms, despite expending minimal manual effort.1