The promises inherent in users coming together to form data sharing network communities, bring to the foreground new problems formulated over such dynamic, ever growing, computing, storage, and networking infrastructures. A key open challenge is to harness these highly distributed resources toward the development of an ultra scalable, efficient search engine. From a technical viewpoint, any acceptable solution must fully exploit all available resources dictating the removal of any centralized points of control, which can also readily lead to performance bottlenecks and reliability/availability problems. Equally importantly, however, a highly distributed solution can also facilitate pluralism in informing users about internet content, which is crucial in order to preclude the formation of information-resource monopolies and the biased visibility of content from economically-powerful sources. To meet these challenges, the work described here puts forward MINERVAâï申ï申, a novel search engine architecture, designed for scalability and efficiency. MINERVAâï申ï申 encompasses a suite of novel algorithms, including algorithms for creating data networks of interest, placing data on network nodes, load balancing, top-k algorithms for retrieving data at query time, and replication algorithms for expediting top-k query processing. We have implemented the proposed architecture and we report on our extensive experiments with real-world, web-crawled, and synthetic data and queries, showcasing the scalability and efficiency traits of MINERVAâï申ï申.