Existing solutions for searching for appropriate music content rely on methods like string edit distances or functional approaches (as for the symbolic level) or features extraction methods (as for the audio level) that cannot provide structural similarity information. Our results in structural similarity measures for music information retrieval rely on graph theory and provide new tools for extract characterizing structural quantities from musical data (both symbolic and audio) to be implemented within an integrated query environment. The improvements regard the use of the graph model of musical data at different levels, the MX format for the representation of musical work and the new user interfaces for the navigation of musical source material.