Semantic caches allow queries into large datasets to leverage cached results either directly or through transformations, using semantic information about the data objects in the cache. As the price of main memory continues to drop and its size increases, the size of semantic caches grows proportionately, and it is becoming expensive to compare the semantic information for each data object in the cache against a query predicate. Instead, we propose to create an index for cached objects. Unlike straightforward linear scanning, indexing cached objects creates additional overhead for cache replacement. Since the contents of a semantic cache may change dynamically at a high rate, the cache index must support fast inserts and deletes as well as fast search. In this paper, we show that multidimensional indexing helps navigate efficiently through a large semantic cache in spite of the additional overhead and overall is considerably less expensive than linear scanning. Little emphasis has been laid upon the performance of multidimensional index inserts and deletes, as opposed to search performance. We compare the performance of a few widely used multidimensional indexing structures with our SH-tree, looking at insert, delete, and search operations, and show that SH-trees overall perform better for large semantic caches than the widely used indexing techniques.