Random restarts for unsatisfiable instances
There is some research in this area.  In The Effect of Restarts on the Efficiency of Clause Learning Jinbo Huang shows empirically that restarts improve a solver's performance over suites of both satisfiable and unsatisfiable SAT instances.  The theoretical justification for the speedup is that in CDCL solvers a restart allows the search to benefit from knowledge gained about persistently troublesome conflict variables sooner than backjumping would otherwise allow the partial assignment to be similarly reset.  In effect, restarts allow the discovery of shorter proofs of unsatisfiability on average.