Research over the past decade has clearly shown that long non-coding RNAs (lncRNAs) are functional. Many lncRNAs can be related to immunity and the host response to viral infection, but their specific functions remain largely elusive. The vast majority of lncRNAs are annotated with extremely limited knowledge and tend to be expressed at low levels, making ad hoc experimentation difficult. Changes to lncRNA expression during infection can be systematically profiled using deep sequencing; however, this often produces an intractable number of candidate lncRNAs, leaving no clear path forward. For these reasons, it is especially important to prioritize lncRNAs into high-confidence “hits” by utilizing multiple methodologies. Large scale perturbation studies may be used to screen lncRNAs involved in phenotypes of interest, such as resistance to viral infection. Single cell transcriptome sequencing quantifies cell-type specific lncRNAs that are less abundant in a mixture. When coupled with iterative experimental validations, new computational strategies for efficiently integrating orthogonal high-throughput data will likely be the driver for elucidating the functional role of lncRNAs during viral infection. This review highlights new high-throughput technologies and discusses the potential for integrative computational analysis to streamline the identification of infection-related lncRNAs and unveil novel targets for antiviral therapeutics.