Dominance pruning methods have recently been introduced for optimal planning. They compare states based on their goal distance to prune those that can be proven to be worse than others. In this paper, we introduce dominance techniques for satisficing planning. We extend the definition of dominance, showing that being closer to the goal is not a prerequisite for dominance in the satisficing setting. We develop a new method to automatically find dominance relations in which a state dominates another if it has achieved more serializable sub-goals. We take advantage of dominance relations in different ways; while in optimal planning their usage focused on dominance pruning and action selection, we also use it to guide enforced hill-climbing search, resulting in a complete algorithm.