Bitmap indexes are known as the most effective indexing methods for range queries on append-only data, especially for low cardinality attributes. Recently, bitmap indexes were also shown to be just as effective for high cardinality attributes when certain compression methods are applied. There are many different bitmap indexes in the literature but no definite comparison among them has been made, largely because there is no accurate prediction of their index sizes and search time. This paper presents a systematic evaluation of two large subsets of compressed bitmap indexes that use multi-component and multi-level encodings. We combine extensive analyses with ample experimental results to confirm them, whereas earlier studies of these indexes are either empirical or for uncompressed indexes only. Our analyses provide highly accurate predictions that agree with test measurements. These analyses not only identify the best methods in terms of index size and query processing cost, but also reveal new ways of using multi-level methods that significantly improve their performance. Using the best parameters obtained through analyses, we produce three two-level indexes with the optimal computational complexity. Furthermore, the fastest two-level indexes are predicted and observed to be 5 to 10 times faster than other well-known indexes.