The advent of high-throughput biotechnologies, which can efficiently measure gene expression on a global basis, has led to the creation and population of correspondingly rich databases and compendia. Such repositories have the potential to add enormous scientific value beyond that provided by individual studies which, due largely to cost considerations, are typified by small sample sizes. Accordingly, substantial effort has been invested in devising analysis schemes for utilizing gene-expression repositories. Here, we focus on one such scheme, the Connectivity Map (cmap), that was developed with the express purpose of identifying drugs with putative efficacy against a given disease, where the disease in question is characterized by a (differential) gene-expression signature. Initial claims surrounding cmap intimated that such tools might lead to new, previously unanticipated applications of existing drugs. However, further application suggests that its primary utility is in connecting a disease condition whose biology is largely unknown to a drug whose mechanisms of action are well understood, making cmap a tool for enhancing biological knowledge. The success of the Connectivity Map is belied by its simplicity. The aforementioned signature serves as an unordered query which is applied to a customized database of (differential) gene-expression experiments designed to elicit response to a wide range of drugs, across of spectrum of concentrations, durations, and cell lines. Such application is effected by computing a per experiment score that measures "closeness" between the signature and the experiment. Top-scoring experiments, and the attendant drug(s), are then deemed relevant to the disease underlying the query. Inference supporting such elicitations is pursued via re-sampling. In this paper, we revisit two key aspects of the Connectivity Map implementation. Firstly, we develop new approaches to measuring closeness for the common scenario wherein the query constitutes an ordered list. These involve using metrics proposed for analyzing partially ranked data, these being of interest in their own right and not widely used. Secondly, we advance an alternate inferential approach based on generating empirical null distributions that exploit the scope, and capture dependencies, embodied by the database. Using these refinements we undertake a comprehensive re-evaluation of Connectivity Map findings that, in general terms, reveal that accommodating ordered queries is less critical than the mode of inference.