Similarity search is a fundamental operation for analyzing data series (DS), which are ordered sequences of real values. To enhance efficiency, summarization techniques are employed that reduce the dimensionality of DS. SAX-based approaches are the state-of-the-art for exact similarity queries, but their performance degrades for high-frequency signals, such as noisy data, or for high-frequency DS. In this work, we present the SymbOlic Fourier Approximation index (SOFA), which implements fast, exact similarity queries. SOFA is based on two building blocks: a tree index (inspired by MESSI) and the SFA symbolic summarization. It makes use of a learned summarization method called Symbolic Fourier Approximation (SFA), which is based on the Fourier transform and utilizes a data-adaptive quantization of the frequency domain. To better capture relevant information in high-frequency signals, SFA selects the Fourier coefficients by highest variance, resulting in a larger value range, thus larger quantization bins. The tree index solution employed by SOFA makes use of the GEMINI-approach to answer exact similarity search queries using lower bounding distance measures, and an efficient SIMD implementation. We further propose a novel benchmark comprising 17 diverse datasets, encompassing 1 billion DS. Our experimental results demonstrate that SOFA outperforms existing methods on exact similarity queries: it is up to 10 times faster than a parallel sequential scan, 3–4 times faster than FAISS, and 2 times faster on average than MESSI. For high-frequency datasets, we observe a remarkable 38-fold performance improvement.