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We analyze computational aspects of variational approximate inference techniques for sparse linear models, which have to be understood to allow for large scale applications. Gaussian covariances play a key role, whose approximation is computationally hard. While most previous methods gain scalability by not even representing most posterior dependencies, harmful factorization assumptions can be avoided by employing data-dependent low-rank approximations instead. We provide theoretical and empirical insights into algorithmic and statistical consequences of low-rank covariance approximation errors on decision outcomes in nonlinear sequential Bayesian experimental design.