Inverted indexes are the backbone of most large-scale information retrieval systems. Although conceptually simple, high-performance inverted indexes require a deep understanding of low-level system optimizations, memory layouts, data compression techniques, and index traversal strategies. With the widespread adoption of in-memory search engines, the rise of learned sparse retrieval (LSR), and the increasing complexity of ranking pipelines, the design space for efficient indexing and retrieval systems has expanded significantly. This tutorial addresses a critical knowledge gap between textbook-style explanations and advanced techniques required for efficient and optimized retrieval. It aims to equip researchers and practitioners with a comprehensive understanding of how modern in-memory search systems are designed, built, and optimized for high-performance retrieval across large-scale document collections.