In this paper we present a QBF solver that is based on BDD technologies but includes optimizations from search-based algorithms. We enhance the early quantification technique from model checking, favoring aggressive quantification over conjunction of BDDs. BDD Constraint propagation is also described, a strategy inspired by the efficient simplifications applied to CNFs in DPLL-based algorithms . Some dynamic variable elimination heuristics that enforce quantification and bounded space usage are also presented, coping with the difficulties faced by static heuristics included in previous BDD-based solvers. Experimental results show that our solver outperforms both symbolic and searchbased competitive solvers in formal verification benchmarks with practical applications in equivalence checking and theorem proving, by completing more problems or finishing in less time. Some preliminary results also show that the solver is able to handle some other hard problems for symbolic solvers in the planning domain with similar efficiency. The benchmarks we used contain QBFs of nearly up to 9000 variables and are available at the QBFLIB website.