Relaxed plans are used in the heuristic search planner FF for computing a numerical heuristic and extracting helpful actions. We present a novel way for extracting information from the relaxed plan and for dealing with helpful actions, by considering the high quality of the relaxed plans. In numerous domains, the performance of heuristic search planning and the size of the problems that can be handled have been drastically improved. 1 Computing and using lookahead states In classical forward state-space search algorithms, a node in the search graph represents a planning state and an arc starting from that node represents the application of one action to this state, that leads to a new state. In order to ensure completeness, all actions that can be applied to one state must be considered. The order in which these states will then be considered for development depends on the overall search strategy: depth-first, breadth-first, best-first. . . Let us now imagine that for each evaluate...