In the age of information overload, intelligent agents have proven themselves as a very useful tool for discovering information of interest on the Web. The information seeking process may be either static, by utilizing existing search engines or using collaborative techniques, or dynamic, by actively browsing the Web. In the second case, agents need to evaluate encountered hyperlinks and choose the promising ones for continuing their autonomous navigation. In this paper we describe a new learning method for identifying interesting links in autonomous information discovery and we present the preliminary results from applying the new method into difficult query domains.