Efficient localization of query-similar subsequences in a database of untrimmed 3D human motion data is crucial to applications in numerous domains. We propose a novel subsequence search approach that partitions untrimmed database motions into segments across a few levels to accommodate variably-sized queries, addressing the limitations of single- and many-level state-of-the-art methods. By determining a deep similarity between the query and database segments, we specifically identify larger regions within the database motions likely to contain query-similar subsequences. These regions are then narrowly examined to determine the precise location of relevant subsequences, considering also variations in execution speed. While this approach contributes to a high retrieval quality, it also requires high search costs. Therefore, we propose two filtering techniques that further decrease the number of examined subsequences by more than an order of magnitude on a newly established benchmark across four challenging PKU-MMD sub-datasets.