We propose a new approach based on ranking to learn to guide Greedy Best-First Search (GBFS). As previous ranking approaches, ours is based on the observation that directly learning a heuristic function is overly restrictive, and that GBFS is capable of efficiently finding good plans for a much more flexible class of total quasi-orders over states. In order to learn an optimal ranking function, we introduce a new framework capable of leveraging any neural network regression model and of efficiently handling the training data through batching. Compared with previous ranking approaches for planning, ours does not require complex loss functions and allows training on states outside of the optimal plan with minimal overhead. Our experiments on the domains of the latest planning competition learning track shows that our approach greatly improves the coverage of the underlying neural network models without degrading plan quality.