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Large-eddy simulations (LES) of turbulent flames with detailed finite-rate kinetics is currently computationally infeasible due to the enormous cost associated with computation of reaction kinetics. Recently, an In-Situ Adaptive Tabulation (ISAT) methodology was shown to reduce the cost of direct integration considerably. However, ISAT tables require significant on-line storage in memory and may result in restriction on massivelly parallel systems. Furthermore, application of ISAT in LES requires re-evaluation of the tree structure and the access/retrieval process. Here, issues regarding the use of ISAT in a LES are discussed. Then, a storage-efficient Artificial Neural Network (ANN) is trained using ISAT data and used to simulate turbulent premixed flames in both the thin-reaction-zone and flamelet regimes. Finally, the issues to be addressed in order to apply this combined ISAT/ANN methodology for full-scale LES of reacting flows are discussed.