Computational Grids provide a widely distributed computing environment suitable for randomized SAT solving. This paper develops techniques for incorporating learning, known to yield significant speed-ups in the sequential case, in such a distributed framework. The approach exploits existing state-of-the-art clause learning SAT solvers by embedding them with virtually no modifications. We show that for many industrial SAT instances, the expected run time can be decreased by carefully combining the learned clauses from the distributed solvers. We compare different parallel learning strategies by using a representative set of benchmarks, and exploit the results to devise an algorithm for learning-enhanced randomized SAT solving in Grid environments. Finally, we experiment with an implementation of the algorithm in a production level Grid and solve several problems which were not solved in the SAT 2007 solver competition.