Reduced left ventricular ejection fraction (LVEF) is an important marker of cardiovascular risk, but echocardiography is not always readily available. We investigated whether multimodal contrastive pretraining with echocardiography improves ECG-based LVEF estimation. Two ECG pipelines were aligned with frozen EchoPrime representations from multi-view studies or apical four-chamber (A4C) videos, including phase-aware variants. The encoders were combined with a beta mixture density head and evaluated in frozen and fine-tuned settings. Models were trained on MIMIC-IV and externally evaluated on EchoNext. The fine-tuned A4C-only model achieved the highest AUROC (0.7886), while its phase-aware variant achieved the lowest MAE (7.92%). With frozen DINO-based encoders, the phase-aware A4C-only alignment performed best (AUROC 0.7741; MAE 8.56%). Multimodal pretraining improved frozen DINO models over the unimodal baseline, but its benefits diminished after fine-tuning, suggesting that encoder initialization and downstream adaptation were more influential than increasingly complex echocardiographic supervision.