For multi-hop queries in retrieval-augmented generation (RAG), it is crucial to accurately obtain evidence pieces. In this study, we propose a training-free method for multi-hop query retrieval, named Intrinsic Navigating-based Non-Generative Retrieval (IN-Retriever). Instead of generating intermediate reasoning text, the method extracts the bridging clue from the internal attention of the embedding model based on the current context and retrieved candidate documents. During iterative clue extraction and document retrieval, IN-Retriever strips away revealed clues from the embedded query vector and then reranks candidate documents in a targeted manner, thereby steering retrieval toward hidden next-hop evidence. In the experiments, we confirm the feasibility and effectiveness of using intrinsic attention signals to guide external multi-hop retrieval. Experimental results show that our method precisely collects sufficient documents to support multi-hop queries and significantly outperforms sparse and dense retrieval methods.