Providing \textit{structured} information to large language models (LLMs) improves multi-step reasoning and factual grounding, and recent retrieval-augmented generation (RAG) systems therefore reconstruct structure from retrieved text on every query. When the corpus is \emph{already} structured --- as in JSON Lines (JSONL), a popular format for LLM prompts, chemical compounds, and geospatial records --- this per-query rebuilding can be replaced by direct \emph{structural retrieval}. The core primitive is \textit{substructure search}: finding all JSON objects in a collection that contain a given query pattern. Existing approaches index each document separately, so both index space and query time grow with the total collection size; XML-based engines add conversion overhead and semantic mismatches. We propose \textbf{jXBW}, a compressed index for fast substructure search over JSONL, combining three innovations: (i) a merged tree representation that consolidates repeated structures across objects, (ii) a succinct tree index based on the eXtended Burrows--Wheeler Transform (XBW), and (iii) a newly developed three-phase substructure search algorithm that runs on this index. Together they achieve \textbf{query-dependent complexity}: \chgb{the search avoids a full scan of the collection and is candidate- and output-sensitive}, in compressed space. Experiments on seven real-world datasets, including PubChem ($10^6$ compounds) and OpenStreetMap ($6.6 \times 10^6$ objects), show that jXBW outperforms the strongest tree-based baseline by $\mathbf{16\times}$ on the smallest dataset and by up to $\mathbf{2{,}800\times}$ on the largest, and is more than $\mathbf{2 \times 10^6\times}$ faster than the XQuery engine Saxon. jXBW thus brings structural retrieval over million-record JSONL collections into the sub-millisecond range.

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