  A key challenge in automated chemical compound space explorations is ensuring
veracity in minimum energy geometries---to preserve intended bonding
connectivities. We discuss an iterative high-throughput workflow for
connectivity preserving geometry optimizations exploiting the nearness between
quantum mechanical models. The methodology is benchmarked on the QM9 dataset
comprising DFT-level properties of 133,885 small molecules; of which 3,054 have
questionable geometric stability. We successfully troubleshoot 2,988 molecules
and ensure a bijective mapping between desired Lewis formulae and final
geometries. Our workflow, based on DFT and post-DFT methods, identifies 66
molecules as unstable; 52 contain $-{\rm NNO}-$, the rest are strained due to
pyramidal sp$^2$ C. In the curated dataset, we inspect molecules with long CC
bonds and identify ultralong contestants ($r>1.70$~\AA{}) supported by
topological analysis of electron density. We hope the proposed strategy to play
a role in big data quantum chemistry initiatives.
