Angular similarity measures have been utilized by several database applications to define semantic similarity between various data types such as text documents, time-series, images, and scientific data. PrProblems due to a mismatch in the underlying geometry as well as the high dimensionality of the data results in poor performance. We focus on indexing and query processing for angular similarity queries. In particular, we developed access structures based on quantization tailored on the angular orientation of the data objects, and algorithms that utilize these structures as dense indices. We have promising results that are scalable with respect to dimensionality and the size of the data.