Recently, peer-to-peer systems have become one of the most popular distributed applications. Many previous works have investigated identifier-based indexing systems that support a query-by-identifier service. However, clients usually have only partial information about an object, and prefer to query by keywords. In this paper, we propose a Small-World-based Key-word Search System (SW-KSS) that provides keyword search and similarity search services simultaneously. The proposed SWKSS applies the concept of the "small world theory" to the construction of an indexing structure. Such structures mirror the way humans keep track of their friends and acquaintances; hence, they can cluster peers who share common interests. The method enables a peer to find objects of interest from similar neighboring peers efficiently. We evaluate the performance of SW-KSS via simulations. The results show that SW-KSS can achieve both scalability and partial-match look-up capability.