In this study, we apply the automated optimization strategy implemented in SwarmCG for evaluating two putative refined CG representations of the lipid models in the framework of Martini, aiming at improving their thermodynamic properties. Because we optimize a finite number of bonded parameters in an informative context (i.e. rich training sets and non-bonded parameters remain constant, set to Martini 3.0.0 4 ), we can eliminate uncertainties related to parameters tuning for focusing on evaluating the capabilities and limits of the CG representations (i.e. the choice of beads, their reference positioning, the topologies of the CG models and potentials used to describe bonded interactions). Our results indicate both Representations 1 and 2 allowed to improve upon the current version of the PC lipid models in Martini 3.0.0 4 (based on the 9 different lipid types included in the benchmark and on the objectives set) and the protocol implemented in SwarmCG is effective in this context for optimizing up to 77 bonded parameters (and likely more). Using mixed bead sizes in Representation 1 allowed to match the experimental objectives particularly in the liquid phases. The description of the phase behavior verified a posteriori for DOPC/DPPC mixtures appears relevant as well, but the DPPC model in this CG representation is a special case, that includes enough repeated big beads in the tails to enable the formation of a gel phase (which is not the case for DMPC and DSPC). Conclusively, the philosophy of Representation 1 cannot yield versatile building blocks for describing multiple types of lipids across phase states.