The　challenge　of　cross-domain　few-shot　learning　(CD-FSL)　stems　from　the　substantial　distribution　disparities　between　target　and　source　domain　images,　necessitating　a　model　with　robust　generalization　capabilities.　In　this　work,　we　posit　that　large-scale　pretrained　models　are　pivotal　in　addressing　the　CD-FSL　task　owing　to　their　exceptional　representational　and　generalization　prowess.　To　our　knowledge,　no　existing　research　comprehensively　investigates　the　utility　of　large-scale　pretrained　models　in　the　CD-FSL　context.　Addressing　this　gap,　our　study　presents　an　exhaustive　empirical　assessment　of　the　Contrastive　Language-Image　Pre-Training　model　within　the　CD-FSL　task.　We　undertake　a　comparison　spanning　six　dimensions:　base　model,　transfer　module,　classifier,　loss,　data　augmentation,　and　training　schedule.　Furthermore,　we　establish　a　straightforward　baseline　model,　E-base,　based　on　our　empirical　analysis,　underscoring　the　importance　of　our　investigation.　Experimental　results　substantiate　the　efficacy　of　our　model,　yielding　a　mean　gain　of　1.2%　in　5-way　5-shot　evaluations　on　the　BSCD　dataset.