This paper describes the first implementation of POIROT, a web search agent based on relevance, that determines the users interests inspecting the pages bookmarked in the web browser and extracting keywords using some information theory methods such as TF-IDF. The keywords are used to build a training set that is processed by an Inductive Logic Programming (ILP) algorithm that learns what is "relevant" to the user. The rules generated with ILP are used to expand user queries and to rank the results. POIROT also models the behavior of the more important Internet search engines to determine which one to use depending on the topic to search. One important design consideration of POIROT is to build its models without asking the user for feedback, from this perspective POIROT is an active learner. Some comparisons with Metacrawler are reported, showing that POIROT outperforms in terms of relevance and precision of the results presented.