This paper presents a tunable content-based music retrieval (CBMR) system suitable for retrieval of music audio clips. Audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The tuning process utilizes our genetic algorithm that optimizes cluster compactness. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently conducted because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(log n) time complexity. The paper includes experimental results in measuring retrieval performance in terms of precision and recall. Significant improvement in retrieval performance over the untuned feature space is reported.