When it comes to learning control knowledge for plan-ning, most works focus on "how to do it" knowledge which is then used to make decisions regarding which actions should be applied in which state. We pursue the opposite approach of learning "how to not do it" knowledge, used to make decisions regarding which ac-tions should not be applied in which state. Our intuition is that "bad actions" are often easier to characterize than "good" ones. An ob-vious application, which has not been considered by the few prior works on learning bad actions, is to use such learned knowledge as action pruning rules in heuristic search planning. Fixing a canonical rule language and an off-the-shelf learning tool, we explore a novel method for generating training data, and implement rule evaluators in state-of-the-art planners. The experiments show that the learned rules can yield dramatic savings, even when the native pruning rules of these planners, i.e., preferred operators, are already switched on.