The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectively reason over long horizons, but assume access to a local policy and distance metric over collision-free paths. Reinforcement learning excels at learning policies and relative values of states, but fails to plan over long horizons. Despite the successes of each method on various tasks, long horizon, sparse reward tasks with high-dimensional observations remain exceedingly challenging for both planning and reinforcement learning algorithms. Frustratingly, these sorts of tasks are potentially the most useful, as they are simple to design (a human only need to provide an example goal state) and avoid injecting bias through reward shaping. We introduce a general-purpose control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks. Our main idea is to decompose the task of reaching a distant goal state into a sequence of easier tasks, each of which corresponds to reaching a particular subgoal. We use goal-conditioned RL to learn a policy to reach each waypoint and to learn a distance metric for search. Using graph search over our replay buffer, we can automatically generate this sequence of subgoals, even in image-based environments. Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over hundreds of steps, and generalizes substantially better than standard RL algorithms.