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Boolean satisfiability (SAT) has played a key role in diverse areas spanning planning, inferencing, data mining, testing and optimization. Apart from the classical problem of checking Boolean satisfiability, generating random satisfying assignments has attracted significant theoretical and practical interests over the years. For practical applications, a large number of satisfying assignments for a given Boolean formula are needed, the generation of which turns out to be a hard problem in both theory and practice. In this work, we propose a novel approach to derive a large set of satisfying assignments from a given one in an efficient way. Our approach is orthogonal to the previous techniques for generating satisfying assignments and could be integrated into the existing SAT samplers. We implement our approach as an open-source tool ESampler and conduct extensive experiments on real-world benchmarks. Experimental results show that ESampler performs better than three state-of-the-art samplers on a large portion of the benchmarks, and is at least comparable on the others, showcasing the efficacy of our approach. © 2021, Springer Nature Switzerland AG.