Open-domain textual question answering (QA) systems have been a hot topic in Natural Language Processing (NLP) for quite some time. These systems are designed to find answers from a large amount of textual sources, such as Wikipedia or search engines. Due to the rapid development of deep learning, the performance of QA systems have been significantly improved, especially on machine reading comprehension. In this chapter, we provide an overview of open-domain QA systems, then introduce models on paragraph ranking, candidate answer extraction, and answer selection.