Music similarity has been widely studied through melodic and harmonic matching, clustering, and using various metrics for measuring distance. Such analyses offer the musicologist a view of the ‘sameness’ of parts of a score. However, similarity alone does not necessarily allow exploitation of that sameness in reasoning about the music. In this paper, we present work in progress to investigate rhythm similarity at various scales, beginning at the smallest (single measures or groups of measures). We use normalised compression distance and variations thereof to derive similarity-based dependencies between parts of the music. Establishing such dependencies may allow software engineering dependence analysis techniques to be applied to music to, e.g. remove from focus aspects not relevant to a particular enquiry (‘slicing’), determine the sensitivity of later parts of the music on former parts (‘impact analysis’), and to find motivic processes and developments within the musical form. The analysis will thus draw on software engineering techniques, information theory, and data compression. Our results thus far show that text-based compressors introduce significant non-linear artefacts at small scales making similarity identification based on compressed lengths difficult. Future work will involve progressively larger scale music to determine the sensitivity of the results to the size of music being analysed in order to guide musicologists wanting to adopt similar approaches. We expect to find that at larger scales, the artefacts in text compression become less significant and identifying the threshold at which this happens is thus important. We discuss tree compression as having the potential to capture musically-important relationships lost by text compression and believe that this approach would be more successful at small scales.