First responders equipped with PDAs in disaster recovery scenarios may need to make decisions that select the best out of multiple options. Each of these options may originate from different parts of the network (e.g., risk assessment of various entrances to a collapsed building). Yet the highest priority option needs to be spread to all nodes within a time deadline and in an efficient manner, i.e., by preventing lower priority options from spreading too far. We define this problem as PriorityCast, and study a wide range of possible solutions. The emphasis is on solutions that are scalable, and resilient to unreliability and unpredictability. Our basic scheme, called Flood and Suppress, suppresses lower priority options from being forwarded by a node that has seen other higher priority options. We then augment this basic scheme using probabilistic approaches, background gossiping techniques, and delayed propagation. We quantify the impact of using various combinations of the mentioned approaches, by presenting mathematical analysis for the Flood and Suppress protocol, and evaluating a suite of composable PriorityCast protocols via simulations. The results provide insight into the feasibility and scalability of this class of solutions. While the focus of this paper is on the PriorityCast problem, these solutions are also relevant as potential building blocks for other distributed operations in ad-hoc networks.