In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling large planning problems. On the one hand, reachability analysis and model checking have eventually approached AI-Planning. Unfortunately, they typically rely on uninformed blind search. On the other hand, heuristic search and especially lower bound techniques have matured in effectively directing the exploration even for large problem spaces. Therefore, with heuristic symbolic search we address the unexplored middle ground between single state and symbolic planning engines to establish algorithms that can gain from both sides. To this end we implement and evaluate heuristics found in state-of-theart heuristic single-state search planners.