Multi-Agent Search and Rescue (MASAR) plays a vital role in disaster response, exploration, and reconnaissance. However, dynamic and unknown environments pose significant challenges due to target unpredictability and environmental uncertainty. To tackle these issues, we propose PILOC, a framework that operates without global prior knowledge, leveraging local perception and communication. It introduces a pheromone inverse guidance mechanism to enable efficient coordination and dynamic target localization. PILOC promotes decentralized cooperation through local communication, significantly reducing reliance on global channels. Unlike conventional heuristics, the pheromone mechanism is embedded into the observation space of Deep Reinforcement Learning (DRL), supporting indirect agent coordination based on environmental cues. We further integrate this strategy into a DRL-based multi-agent architecture and conduct extensive experiments. Results show that combining local communication with pheromone-based guidance significantly boosts search efficiency, adaptability, and system robustness. Compared to existing methods, PILOC performs better under dynamic and communication-constrained scenarios, offering promising directions for future MASAR applications.