This chapter investigates early attempts in information retrieval to tackle the full text of document collections. Underpinning a large number of contemporary applications, from search to sentiment analysis, the concepts and techniques pioneered by Hans Peter Luhn, Gerard Salton, Karen Spärck Jones, and others involve particular framings of language, meaning, and knowledge. They also introduce some of the fundamental mathematical formalisms and methods running through information ordering, preparing the extension to digital objects other than text documents. The chapter discusses the considerable technical expressivity that comes out of the sprawling landscape of research and experimentation that characterizes the early decades of information retrieval. This includes the emergence of the conceptual construct and intermediate data structure that is fundamental to most algorithmic information ordering: the feature vector.