Several Web sites deliver a large number of pages, each publishing data about one instance of some real world entity, such as an athlete, a stock quote, a book. Even though it is easy for a human reader to recognize these instances, current search engines are unaware of them. Technologies for the Semantic Web aim at achieving this goal; however, so far they have been of little help in this respect, as semantic publishing is very limited. We have developed a system, called FLINT, for automatically searching, collecting and indexing Web pages that publish data representing an instance of a certain conceptual entity. FLINT takes as input a small set of labeled sample pages: it automatically infers a description of the underlying conceptual entity and then searches the Web for other pages containing data representing the same entity. FLINT automatically extracts data from the collected pages and stores them into a semi-structured self-describing database, such as Google Base. Also, the collected pages can be used to populate a custom, search engine; to this end we rely on the facilities provided by Google Co-op.