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The identification of molecules in exoplanetary atmospheres is only possiblethanks to the availability of high-resolution molecular spectroscopic data.However, due to its intensive and time-consuming generation process, atpresent, only on order 100 molecules have high-resolution spectroscopic dataavailable, limiting new molecular detections.  Using routine quantum chemistry calculations (i.e., scaled harmonic frequencycalculations using the B97-1/def2-TZVPD model chemistry with median errors of10cm-1), here we present a complementary high-throughput approach to rapidlygenerate approximate vibrational spectral data for 2743 molecules made from thebiologically most important elements C, H, N, O, P and S. Though these data arenot accurate enough to enable definitive molecular detections and does not seekto replace the need for high-resolution data, it has powerful applications inidentifying potential molecular candidates responsible for unknown spectralfeatures. We explore this application for the 4.1 micron (2439cm-1) feature inthe atmospheric spectrum of WASP-39b, listing potential alternative molecularspecies responsible for this spectral line, together with SO2. Furtherapplications of this big data compilation also include identifying moleculeswith strong absorption features that are likely detectable at quite lowabundances, and training set for machine learning predictions of vibrationalfrequencies.  Characterising exoplanetary atmospheres through molecular spectroscopy isessential to understand the planet's physico-chemical processes and likelihoodof hosting life. Our rapidly generated quantum chemistry big data set will playa crucial role in supporting this understanding by giving directions intopossible initial identifications of the more unusual molecules to emerge.

High-throughput Quantum Chemistry: Empowering the Search for Molecular Candidates behind Unknown Spectral Signatures in Exoplanetary Atmospheres