Question about zero-inflated Poisson glmer

Dear group - I am currently fitting a Poisson glmer where I have an 
excess of outcomes that are zero (>95%). I am now debating on how to 
proceed and came up with three options:

1.) Just fit a regular glmer to the complete data. I am not fully sure 
how interpret the coefficients then, are they more optimizing towards 
distinguishing zero and non-zero, or also capturing the differences in 
those outcomes that are non-zero?

2.) Leave all zeros out of the data and fit a glmer to only those 
outcomes that are non-zero. Then, I would only learn about differences 
in the non-zero outcomes though.

3.) Use a zero-inflated Poisson model. My data is quite large-scale, so 
I am currently playing around with the EM implementation of Bolker et 
al. that alternates between fitting a glmer with data that are weighted 
according to their zero probability, and fitting a logistic regression 
for the probability that a data point is zero. The method is elaborated 
for the OWL data in: 
https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls/WRITEUP/owls.pdf

I am not fully sure how to interpret the results for the zero-inflated 
version though. Would I need to interpret the coefficients for the 
result of the glmer similar to as I would do for my idea of 2)? And then 
on top of that interpret the coefficients for the logistic regression 
regarding whether something is in the perfect or imperfect state? I am 
also not quite sure what the common approach for the zformula is here. 
The OWL elaborations only use zformula=z~1, so no random effect; I would 
use the same formula as for the glmer.

I am appreciating some help and pointers.

Thanks!
Philipp